Method and apparatus for utilizing estimated patrol properties and historic patrol records

ABSTRACT

It is an object of the present invention to provide a predictive traffic law enforcement profiler apparatus and method which incorporates a means to determine current location, time, velocity and also incorporates a means to utilize a database derived from historic traffic law enforcement records, crowd sourced records and historical traffic data and also incorporates a predictive processing means to provide historic traffic law enforcement records and estimates of enforced speed limits and enforcement profiles, patrol locations and schedules of traffic law enforcement to a driver.

BACKGROUND OF THE INVENTION

The present invention relates to electronic devices used to provideinformation to drivers, driver assist controllers, automated andautonomous vehicle controllers and more particularly relates to a methodand apparatus for utilizing historical data to predict traffic lawenforcement patrol patterns, locations, schedules, speed traps, andenforcement profile.

It is well known that road condition information is very important todrivers to improve efficiency and safety of travel. In particular it isbeneficial to maximize the amount of relevant road information that isavailable to drivers and present it in an optimally beneficial way.Heretofore, the most common road condition information has been realtime and available from radar detectors for locating immediate trafficlaw enforcement patrol locations, radar detectors equipped with GPS fordetecting locations of red light cameras, fixed speed traps and from theDepartment of Transportation through GPS based vehicle navigationsystems for providing real time road condition data.

However, these techniques primarily provide only real time roadcondition information and do not provide historic and probabilistic orstatistical data. More specifically, data available trom traditionalradar detectors only provides the driver with immediate law enforcementlocations with very little warning. Additionally, current generationradar detectors and smart cell phones equipped with GPS for detectingred light cameras or fixed speed traps only provide fixed location oftraffic law enforcement. Additionally, onboard vehicle navigationsystems provide only near real time road accident, hazard and conditioninformation. Additionally, exclusive crowd sourced databases of trafficlaw enforcement encounters are limited and compromised by accuracy ofreports. Heretofore, none of the existing driver information apparatusprovide the driver with historical statistical and probabilistic dataand none predict likely locations of traffic law enforcement or patrollocations, patrol schedules, enforcement profiles, or enforced speedlimit. Additionally, heretofore, no existing driver assisted, automatedor autonomous vehicles incorporate a means to identify and reporttraffic law enforcement encounters as contributors to crowd sourceddatabases without human driver intervention. Furthermore, no existingdriver assisted, automated or autonomous vehicle controllers provide ameans to utilize real time, historical statistical or probabilistictraffic law enforcement patrol locations, patrol schedules, enforcementprofiles, or enforced speed limit estimates to control movement.

It is an object of the present invention to provide historic traffic lawenforcement patrol information. An additional object of the presentinvention is to utilize historic traffic law enforcement information tostatistically predict the locations, schedules, and enforcement profilewhere drivers are likely to encounter traffic law enforcement patrols,speed traps, fixed and mobile speed cameras, red light cameras andprovide the enforced speed limit.

It is an additional object of the present invention to utilize historictraffic law enforcement patrol citation records to statistically predictthe probabilistic locations of traffic law enforcement patrols,enforcement profiles, speed traps and fixed and mobile speed cameras andred light cameras.

An additional object of the present invention is to provide historic andprobabilistic traffic law enforcement patrol information, derived fromhistorical traffic law enforcement data, and statistically predict thelocations where it is more likely to encounter traffic law enforcementand speed traps and provide maximum safe speeds to avoid citation

An additional object of the present invention is to incorporate driverassisted, automated and autonomous vehicle sourced traffic lawenforcement encounter information with crowd sourced databases.

An additional object of the present invention is to provide real time,historic and probabilistic traffic law enforcement patrol information todriver assisted, automated or autonomous vehicle controllers to controlmovement.

An additional object of the present invention is to utilize real timecrowd sourced traffic law enforcement encounter information to providereal time locations of traffic law enforcement to drivers, driverassisted vehicle controllers, and automated or autonomous vehiclescontrollers.

SUMMARY OF THE INVENTION

It is a general object of the present invention to provide a method forsupplying statistical and historical traffic related data to drivers. Itis a more specific object of the present invention to provide apredictive traffic law enforcement profiler apparatus which incorporatesa means to determine current location, date/time, velocity and alsoincorporates a means to access a database derived from historicaltraffic law enforcement records, historical crowd sourced traffic lawenforcement encounter records, and historical traffic data and alsoincorporates a predictive processing means to statistically predictlikely patrol locations, schedules, enforcement profiles of traffic lawenforcement, enforcement austerity, the enforced speed limit andcombinations thereof, and a means to provide this information todrivers, and to driver assisted, automated or autonomous vehiclecontrollers to control vehicle movement.

It is a more specific object of the present invention to provide apredictive traffic law enforcement profiler apparatus which incorporatesa means to determine current location, date/time, velocity and alsoincorporates a means to access a database derived from historicaltraffic law enforcement records, historical crowd sourced traffic lawenforcement encounter records, and historical traffic data of predictedtraffic law enforcement patrol schedules, locations, enforcementprofiles, enforcement austerity and enforced speed limit andcombinations thereof, and provide this information to drivers.

It is yet another object of the present invention to provide anapparatus and method which provides any combinations of predictedtraffic law enforcement patrol locations, schedules, enforcementprofiles, enforced speed limits, and the locations of fixed and mobilespeed cameras, speed traps, and red light cameras.

It is yet another object of the present invention to provide anapparatus and method which provides historic traffic law enforcementpatrol information and a means for filtering and presenting historictraffic law enforcement data.

It is yet another object of the present invention to provide a methodfor determining likely patrol locations, schedules, enforcementprofiles, enforcement austerity, enforced speed limit, locations ofspeed traps, speed cameras, red light cameras, and combinations thereof,of traffic law enforcement and providing this information to consumers.

It is yet another object of the present invention to incorporate driverassisted, automated and autonomous vehicle sourced traffic lawenforcement encounter information with crowd sourced databases oftraffic law enforcement encounter records.

It is yet another object of the present invention is to provide realtime, historic and probabilistic traffic law enforcement patrolinformation to driver assisted, automated or autonomous vehiclecontrollers to control movement.

It is yet another object of the present invention is to utilize realtime crowd sourced traffic law enforcement encounter information toprovide real time locations of traffic law enforcement to drivers,driver assisted vehicle controllers, and automated or autonomousvehicles controllers.

The present invention provides an innovational design which incorporatesstate of the art data processing predictive technology to provideprecise action, increased accuracy, lower cost, and added functionalityover known existing products.

In a preferred embodiment, the predictive traffic law enforcementprofiler apparatus includes a location determining means, a velocitydetermining means, direction determining means, current time and datedetermining means, a database means, user control means, a predictiveprocessor means, an indicator means, a computer interface means, avehicle controller means, a vehicle sensor input means, and a networkinterface communication means. Said database means may contain anycombinations of real time and historic records of traffic lawenforcement encounters. Wherein said database means may contain recordsof estimated properties of traffic law enforcement. Said predictiveprocessing means accesses said database means to provide estimatedtraffic law enforcement profiles and may perform any algorithmicfunctions including any combinations of statistical analysis,inferential statistics, data analytics and artificial intelligence onsaid real time and historic records of traffic law enforcementencounters to predict said properties of traffic law enforcement. Saidvehicle controller means provides ability for said predictive processingmeans to control vehicle speed and preferably vehicle direction androute. Said indicator means provides an ability for said predictiveprocessing means to provide said properties of traffic law enforcementpreferably using both visual, audible, and electronic signalannunciators. Said predictive traffic law enforcement profiler apparatusfurther may be partitioned into one or more client means and one or moreserver means wherein said client means may communicate with said servermeans through said network interface communication means. Wherein saidclient means preferably comprises said location determining means, saidvelocity determining means, said direction determining means, saidcurrent time and date determining means, said user input means, saidindicator means, said vehicle controller means, and said vehicle sensorinput means. Wherein said server means preferably comprises said adatabase means and said predictive processor means.

In another preferred embodiment, the predictive traffic law enforcementprofiler apparatus includes a database means, a predictive processormeans, and an indicator means. The database means includes a means forproviding the locations where traffic law enforcement has historicallyissued citations and the information associated with said citationswhich preferably includes combinations of type of violation, directionof travel, speed of vehicle if cited for speeding, location time anddate and may also include reason for stop and type of vehicle. Saidpredictive processing means cross correlates records in the database tostatistically predict the locations and schedules at which it is moreprobable to encounter traffic law enforcement, and enforcement profilefurther including enforcement austerity, speed traps, speed cameras, redlight cameras and also estimates the enforced speed limit andcombinations thereof, of traffic law enforcement. Said indicator meanswhich preferably includes visual and or audible annunciators presentscombinations of predicted locations, schedules and enforcement profilefurther including enforcement austerity, speed traps, speed cameras, redlight cameras and also estimated enforced speed limit at said locations,of traffic law enforcement.

In another preferred embodiment, the predictive traffic law enforcementprofiler apparatus includes a means to provide estimates of patrolschedules, locations and enforcement profile and combinations thereof,of traffic law enforcement at a singular or plurality of locationscomprising: a database means, access means and an indicator means. Saiddatabase means provides records, derived from issued citations oftraffic law enforcement and crowd sourced encounters with traffic lawenforcement and combinations thereof, of patrol schedules, patrollocations, and estimated enforcement profile including any combinationsof: enforced speed limits, speed traps, speed cameras, red light camerasand enforcement austerity of traffic law enforcement. Said access meansto retrieve combinations of said patrol schedules, patrol locations, andenforcement profile including any combinations of: enforced speedlimits, speed traps, speed cameras, red light cameras and enforcementausterity of traffic law enforcement. Said indicator means to presentcombinations of said patrol schedules, patrol locations, and estimatedenforcement profile including any combinations of: enforced speedlimits, speed traps, speed cameras, red light cameras of traffic lawenforcement.

In another preferred embodiment, the predictive traffic law enforcementapparatus for providing estimates of the enforced speed limit, patrolschedules and enforcement profiles of traffic law enforcement andcombinations thereof at a singular or plurality of first locationscomprising: a database means, location and velocity determining means,time determining means, record access means and indicator means. Saiddatabase means for providing records of estimated traffic lawenforcement patrol schedules, enforced speed limits and enforcementprofiles derived from a combination of issued citations and crowdsourced encounters of traffic law enforcement at a second plurality oflocations. Said location and velocity determining means for determiningthe location and current velocity of said apparatus. Said record accessmeans to retrieve combinations of said estimated traffic law enforcementpatrol schedules, enforced speed limits and enforcement profiles at saidfirst locations from said database means. Said indicator means topresent combinations of said estimated patrol schedules, enforced speedlimits, and enforcement profiles of traffic law enforcement at saidfirst locations. Said indicator means to notify when said apparatuslocation is within or approaching locations of said estimated patrolschedules, enforcement profiles and combinations thereof; and a secondindicator means to notify when said apparatus velocity exceeds saidestimated enforced speed limit at said location of said apparatus.

In another preferred embodiment, the predictive traffic law enforcementprofiler apparatus includes a location determining means, current timeand date determining means, a database means, user input means, apredictive processor means and an indicator means. Said locationdetermining means includes a means to determine the latitude andlongitude location and current velocity. Said time of day determiningmeans includes a means to determine the current date and time. Saiddatabase means includes a means for providing the locations wheretraffic law enforcement has historically issued citations and theinformation associated with said citations which preferably includescombinations of type of violation, direction of travel, violation speedif cited for speeding, location time and date and may also includereason for stop and type of vehicle. Said predictive processing meanscorrelates current location, velocity, time of day, and user criteriawith entries in the database to statistically predict the locationswhere it is more likely to encounter traffic law enforcement, red lightcameras, speed cameras and speed traps and provide maximum safe speedsto avoid citation and provide said information via the indicator meanswhich preferably includes both visual and audible annunciators.

In yet another preferred embodiment, the predictive traffic lawenforcement profiler apparatus includes a client means and a servermeans. Said client means includes a location determining means, a timeof day determining means, an indicator means and a means to communicatewith one or more said server means. Said server means preferablyincludes a database means, a predictive processor means and a means tocommunicate with one or more clients. Said server database meansincludes a means for providing the locations where traffic lawenforcement has historically issued citations and the informationassociated with said citations which preferably includes combinations oftype of violation, direction of travel, speed of vehicle if cited forspeeding, location time and date and may also include reason for stopand type of vehicle. Said client location determining means includes ameans to determine the latitude and longitude location and currentvelocity. Said client time of day determining means includes a means todetermine the current date and time. Said client communication meansprovides a means to communicate the client location, client time of day,and client velocity to said server means. Said server predictiveprocessor means correlates said client location, said client velocity,said client time of day, and user client criteria with records in saidserver database means to statistically predict the locations where it ismore likely for said client to encounter any combinations of traffic lawenforcement, speed traps, red light cameras, speed cameras and providemaximum safe client speeds to avoid citation derived from said serverdatabase of historical issued citations and provide said predictedlocations, speed traps, speed cameras and maximum client speeds to avoidcitation through said server communication means to said client. Saidclient indicator means preferably includes combinations of visual andaudible annunciators to present any combinations of said serverpredicted locations of traffic law enforcement, speed traps, red lightcameras, speed cameras and maximum client speeds to avoid citation.

In yet another preferred embodiment the predictive traffic lawenforcement profiler apparatus includes a means to provide estimates oftraffic law enforcement austerity comprising: a location determiningmeans, database means, database access means, and an indicator means.Wherein said database means comprising estimates of enforcementausterity derived from historic records of issued citation warnings,historic records of traffic flow rates and volumes, records of issuedcitations of traffic law enforcement, and crowd sourced traffic lawenforcement encounters and combinations thereof. Said access means toretrieve a combination of said estimated enforcement austerity and anindicator means to present enforcement austerity at said location.

In yet another preferred embodiment, a method is for providing estimatesof the enforced speed limit, patrol schedules, enforcement profile, andcombinations thereof, of traffic law enforcement at a singular orplurality of locations comprising the steps of: Retrieving a historicalrecord derived from issued traffic law enforcement citations at saidlocation. Correlating citation time, location, violation velocity andcombinations thereof to determine enforced speed limit, patrolschedules, and enforcement profile of traffic law enforcement at saidlocations; presenting said estimated enforced speed limit, estimatedpatrol schedules, enforcement profiles, and combinations thereof, atsaid locations.

In yet another preferred embodiment, the predictive law enforcementtraffic profiler driver information apparatus also includes a means formonitoring current weather conditions and a database means. The databasemeans includes a means for storing the coordinate locations whereaccidents have occurred and the recorded details associated with saidaccidents which preferably includes cause of accident, time of saidaccident, and weather conditions at time of said accident. In thispreferred embodiment, the said predictive processing means correlatescurrent location and current weather conditions with said database todetermine relevant locations of probable road hazards via the indictormeans which preferably includes both visual and audible annunciators.

Further objects and advantages of the present invention will becomeapparent to those skilled in the art from a consideration of thefollowing detailed description of the preferred embodiment and drawings.

DESCRIPTION OF DRAWINGS

FIG. 1 Block diagram of historic and predictive traffic law enforcementprofiler apparatus FIG. 2 A preferred method for profiling traffic lawenforcement and providing historic traffic enforcement records FIG. 3 Apreferred method of predicting patrol schedules at a location FIG. 4 Apreferred presentation of speed limit law enforcement profile FIG. 5 Apreferred presentation of historic traffic law enforcement data andestimated enforcement profiles, patrol locations, and patrol schedulesof traffic law enforcement.

FIG. 6 A diagram showing predictive traffic law enforcement profilerapparatus integrated into a driver assisted or autonomous vehicle.

DETAILED DESCRIPTION

It is well known that traffic law enforcement agencies have patrolpatterns, enforcement profiles and schedules that vary by location, timeof day, month and year, weather conditions and traffic flow rates.Generally, traffic law enforcement typically allow some flexibility inenforcing regulations; examples of which could include allowingmotorists to exceed the posted speed limit by some margin before issuinga citation or allowing motorists extra time on expired vehicleregistrations. The local traffic law enforcement customs may vary bylocation and time. For instance, in some areas the posted speed limitmay be enforced strictly, while in other areas traffic law enforcementmay allow much higher speed limits over the posted speed limits prior toissuing a speeding citation. Additionally, it is not uncommon fortraffic speed limits to be enforced more rigorously at night than duringthe day or during certain holidays there may be increased traffic lawenforcement patrols and the enforcement profile may be more strict andmore focused in certain regions. Additionally, the use of fixed andmobile automated traffic law enforcement systems are commonly used toenforce traffic laws, examples of which include Red Light cameras whichphotograph and issue citations to operators or registered owners ofvehicles passing through an intersection with a red light and SpeedCamera systems which photograph and issue a speeding citation tooperators or registered owners of vehicles exceeding the speed limit ata location. Automated traffic law enforcement systems are typically atfixed locations, or change locations periodically and also frequentlyexhibit predetermined enforcement profiles, which could include theenforced speed limit.

In the present invention, the terms estimated patrol schedule, patrollocations and enforcement profiles are defined in Table 1 tocharacterize the enforcement patterns of traffic law enforcement andutilized in subsequent descriptions of the present invention.

TABLE 1 Traffic Law Enforcement Properties and Patterns which includescombinations of: Estimated and Predicted Patrol Schedules, PatrolLocations, and Enforcement Profiles. Traffic Law Enforcement PropertyDescription Location The location for which these enforcement propertiespertain. Patrol The times at which traffic law enforcement is predictedto be present at the Schedule Location and also predicts the times withwhich traffic law enforcement is predicted to not be present at a givenlocation and the confidence of the prediction. Location Patrol Thepredicted intensity with which traffic law enforcement patrols theIntensity Location and may be as a function of time and the confidenceof the prediction. Locations with higher Patrol Location Intensity maybe considered Patrol Locations. Patrol The locations at which trafficlaw enforcement is predicted to patrol and also Locations predicts thelocations which traffic law enforcement is predicted to not patrol andmay be as a function of time and the confidence of the prediction. APatrol Location may have higher Location Patrol Intensity. EnforcementThe estimated and predicted characteristics which describe traffic lawProfile enforcement at the Location which may include any combinationsof:  a) Enforced speed limit at location and time  b) Speed trapenforced location  c) Speed camera enforced location  d) Red lightcamera enforced location  e) Aircraft speed limit enforced location  f)Histogram of enforced traffic laws  g) Histogram of citation speeds  h)Location citation density  i) Enforcement Profile of Traffic Laws  j)Probability of traffic citation at location and time  k) Probability ofencountering traffic law enforcement at location and time  l)Enforcement Austerity - traffic law enforcement inflexibility and   Austereness   a. Enforcement Leniency Profile - probability ofreceiving a citation verses a warning as a function of violation type  b. Speed Limit Enforcement Austerity Relative to Average Traffic FlowVelocity - estimated enforced speed limit relative to the trafficvelocity   c. Speed Limit Enforcement Austerity Relative to AverageTraffic Flow Volume - estimated enforced speed limit relative to thetraffic volume   d. Location Enforcement Austerity Relative to AverageTraffic Flow Volume - estimated amount a location is patrolled relativeto traffic volume

State and city law enforcement agencies maintain databases of trafficviolations that were issued. Examples of traffic violations may includebut are not limited to speeding citations, expired vehicle registration,and other moving and non-moving traffic violations. Each citation recordtypically includes information relevant to the violation. In the case ofspeeding citations, typically the time, date, location, direction andspeed of the vehicle are recorded. In the case of expired registrations,typically the time, date, location, and duration of expiration arerecorded. In the present invention, the term “citation record” or“citation” refers to the collection of data captured with an issuedcitation. Additionally, it is also possible to maintain databases ofcrowd or user sourced data which could include information provided byindividuals that experienced an encounter with traffic law enforcement.Such encounters could include traffic stops by traffic law enforcementand the reason for stop could be provided, location, time, date, andpreferably the speed at which the driver was travelling could beprovided to the database. Additionally, an encounter could also be avehicle passing a traffic law enforcement vehicle in which case thelocation, time, date, direction and preferably speed at which the userwas travelling could be contained in the database. In the presentinvention, the term “encounter record” or “encounter” refers to thecollection of data captured with a crowd sourced encounter event withtraffic law enforcement.

In the present invention the term “driver” refers to a controller of avehicle which includes human or machine controllers or any combinationthereof. Machine controllers provide a level of automation of a vehiclesystem and include sensors to sample the environment, processors toevaluate environmental information and human driver preferences, andactuators to effect vehicle movement. Examples of machine controllersmay include cruise controllers which control the speed of a vehicle,adaptive cruise controllers which control the speed of a vehicle but mayvary the speed based on environmental conditions, lane assistcontrollers which assist a human driver with directional control,semi-autonomous vehicle controllers and fully autonomous vehiclecontrollers which can perform route planning, and control the directionand speed of a vehicle and adapt based on environmental conditions.

In the present invention the term “vehicle” refers to both humancontrolled or automated machine controlled vehicles or combinationsthereof. The level of vehicle machine control can range from exclusivelyhuman control to fully autonomous and can be categorized into fivelevels of automation as presented in Table 2.

TABLE 2 Levels of autonomous vehicles. Level Description 0 No drivingautomation - Full-time performance by the human driver of all aspects ofthe dynamic driving task, even when enhanced by warning or interventionsystems. 1 Driver Assistance - system of either steering oracceleration/ deceleration using information about the drivingenvironment and with the expectation that the human driver performs allremaining aspects of the dynamic driving task. Examples include:stability control, cruise control, and automatic braking 2 PartialDriver Automation - The driving mode-specific execu- tion by one or moredriver assistance systems of both steering and acceleration/decelerationusing information about the driv- ing environment and with theexpectation that the human driver performs all remaining aspects of thedynamic driving task. Examples include: adaptive cruise control incombination with lane keeping. 3 Conditional Driver Automation - Thedriving mode-specific performance by a machine controlled AutomatedDriving System of all aspects of the dynamic driving task with theexpectation that the human driver will respond appropriately to arequest to intervene. 4 High Automation: The driving mode-specificperformance by a machine controlled Automated Driving System of allaspects of the dynamic driving task, even if a human driver does notres- pond appropriately to a request to intervene. May include ma- chinecontrolled automated route planning. 5 Full Automation: The full-timeperformance by a machine controlled Automated Driving System of allaspects of the dynamic driving task under all roadway and environmentalconditions that can be managed by a human driver. Could include fullyautonomous unoccupied vehicles.

In the present invention the term “crowd sourced” refers to contributorsof records of encounters with traffic law enforcement and includes humancontributors and automated contributors. More specifically, sensors onvehicles can identify encounters with traffic law enforcement andautonomously contribute encounter records.

The historic and predictive traffic law enforcement profiler apparatuspreferably utilizes databases of traffic citations, traffic stop, andarrest records maintained by law enforcement agencies including statehighway patrols, city and county police departments, municipal courts,state courts, departments of motor vehicles, and in general governmentor private agencies collective referred to as citations, and preferablyincludes databases of crowd sourced encounters with traffic lawenforcement collectively referred to as encounters to profile andpredict the enforcement profile, patterns, locations and schedules wheretraffic law enforcement agencies patrol. The apparatus provides anindication to a driver when approaching an area where there is ahistoric or statistically significant chance of encountering traffic lawenforcement personnel allowing precaution to be taken such as drivingcautiously and within enforced speed limits. Additionally, the apparatusmay also provide the driver with historically significant traffic lawenforcement information which may include a maximum estimated speedwhich it is safe to drive with an acceptably low chance of being citedby law enforcement for violating the speed limit. Additionally, theapparatus preferably may provide the driver with a historical record oftraffic law enforcement citations and preferably a set of databaseprocessing methods to enable searching, filtering, extracting,statistical processing, and viewing of results preferably includinghistogram creation, distributions, scatter plots, tables and lists.

A block diagram of a preferred embodiment of the historic and predictivetraffic law enforcement profiler apparatus is shown in FIG. 1. As can beseen, the profiler apparatus, 14 preferably consists of a client device21 and a server resource 3. The client device 21 may further include ahistoric and predictive processing unit 1, a location determining unit7, time determining unit 25, direction determining unit 26, velocitydetermining unit 28, vehicle sensor input 27, a historical database 2,real time data base 30, a road map database 15, a traffic flow database16, a visual display 9, an audible output 8, a user control 10, vehiclecontroller 24 and a network interface 6. The server resource 3preferably includes a historic database 4, a real time database 5, atraffic flow database 23, a roadway map database 17, a network interface19, and a predictive processing unit 18. Databases 2, 15, 16, 30, 4, 5,17, 23 are presented as distinct databases; however, those skilled inthe art will recognize that any physical or logical combinations ofdatabases 2, 15, 16, 30, 4, 5, 17, 23 are realizable and consideredwithin the scope of the present invention.

A preferred embodiment of the profiler apparatus 14 includes a clientdevice 21 and a server resource 3. In a more specific preferredembodiment the client device 21 could be implemented as a standalonedevice and the server resource 3 may preferably not be required. Or inanother preferred embodiment the client device 21 could be implementedas a thin device, utilizing server resource 3 for providing databases 4,5, 17, 23 and historic predictive processing unit 18 through the networkinterfaces 6, 19 wherein network interfaces 6, 19 could be eitherwireless or wired and the client device 21 may not require databases 2,15, 16, 30 or predictive processing unit 1. In an additional preferredembodiment, client device 21 and server resource 3 are both present andshare responsibility for providing database content 4, 5, 17, 23, 2, 15,16 and predictive compute resources 1, 18.

In a preferred portable navigation device embodiment, the client device21 of the profiler apparatus 14 could be integrated into a standaloneportable navigation devices such as a TomTom, Garmin, Magellan, Nuvi orsimilar road navigation device in which case preferably the clientdevice 21 could use the common resources of the portable navigationdevice including but not limited to the display 9, audio output 8,location determining unit 7, user control 10, predictive processing unit1 and local databases 2, 15, 16, 30. To provide access to real timedatabases and physically separate historical databases and predictiveprocessing resources, the preferred portable navigation deviceembodiment may also include wired and/or wireless instances of networkinterfaces 6, 19.

Interfaces 6, 19 provide data communication with server resource 3preferably providing records of traffic law enforcement citations andencounters from real time database 5, historical database 4, trafficflow database 23, and map database 17 and preferably server resourcepredictive processing unit 18.

In a preferred smart phone or tablet embodiment, the historic andpredictive client device 21 could be a smart phone or tablet executingan application which may be interoperating with a web applicationexecuting on a web server 3. In the preferred smart phone embodiment,the client device 21 of the profiler apparatus 14 could be integratedwith a smart phone or tablet device such as an iPhone, iPad, Googlephone, Microsoft Surface tablet or device operating the Android OS,Windows OS, iOS or similar device and could use the common resources ofthe device preferably including but not limited to the display 9, audiooutput 8, location determining unit 7, time determining unit 25,direction determining unit 26, velocity determining unit 28, usercontrol 10, predictive processing unit 1 and local databases 2, 15, 16,30. Additionally, the preferred smart phone or tablet embodiment couldoptionally utilize wired or wireless network interfaces 6, 19 whichcould preferably support G3, G4 or other wireless data standards toprovide data communication with a server resource 3 preferably providingtraffic law enforcement citations and encounters from real time database5, historical database 4, traffic flow database 23, and map database 17and preferably server resource predictive processing unit 18.

In a preferred personal computer embodiment, the client device 21 of theprofiler apparatus 14 could be a personal computer and the server device3 could be a web server. In the preferred personal computer embodiment,Client device 21 could use the common resources of the device preferablyincluding but not limited to the computer screen as the display 9,computer speaker output as the audio output 8, location determining unit7, time determining unit 25, graphical user interface as the usercontrol 10, the central processor unit as the predictive processing unitof the personal computer and local databases 2, 15, 16, 30 which couldbe on a computer drive. Additionally, the preferred personal computerembodiment could utilize internet network interfaces 6, 19 to provideinternet data communication with a server resource 3 preferablyproviding traffic law enforcement encounters from real time database 5,historical database 4, traffic flow database 23, and map database 17 andpreferably server resource predictive processing unit 18. Furthermore,the preferred personal computer embodiment could be a web browser orapplication executing on the personal computer client device 21 withserver resource 3 providing predictive processing services in unit 18.

In a thin predictive processor preferred embodiment, databases 2, 15,16, 30 could be combined into a unified database which containscombinations of pre-computed predicted patrol schedules, and enforcementprofiles which may include estimated enforced speed limits, patrolschedules, patrol locations, enforcement profiles, and estimatedenforcement austerity, and other properties as defined in Table 1, at asingular or plurality of locations and a map of the roadway system. Theplurality of locations would preferably include locations and be limitedto within the geographic region of travel. Additionally, the maximumplurality of locations would be limited by the size of the database 2,15, 16, 30. Typically 10 billion locations could be stored with currentclient database technologies however, those familiar with the art, wouldrecognize that future database technologies could exist whichconceivably support additional locations. Additionally, in thispreferred embodiment databases 4, 5, 17, 23 could similarly be combinedinto a unified database which contains combinations of the predictedenforcement profiles, estimated patrol schedules, estimated enforcedspeed limits, and estimated enforcement austerity at a singular orplurality of locations and a map of the roadway system. Typically, 100billion locations could be stored with current server databasetechnologies however, those familiar with the art, would recognize thatfuture database technologies could exist which conceivably supportadditional locations. In the thin predictive processor preferredembodiment, the predictive processor unit of the client 1, and server 18could perform access functions to database 2, 15, 16, 30 and 4, 5, 17,23 respectively, to retrieve any combinations of pattern propertiespresented in Table 1 including for example the predicted enforcementprofiles, estimated patrol schedules, estimated enforced speed limits,and estimated enforcement austerity, at locations of interest andprovide indications to the user through the audio 8 and visual 9.

In a preferred machine controlled or automated vehicle embodiment, theclient device 21, 102 of the profiler apparatus 14 could be integratedinto a vehicle 100. The vehicle 100 could incorporate any level ofautomation presented in Table 2 including driver assistance, partialdriver automation, conditional driver automation, high automation, orfull automation (fully autonomous). and could use the common resourcesof the vehicle 100 preferably including but not limited to the display9, audio output 8, location determining unit 7, time determining unit25, direction determining unit 26, velocity determining unit 28, usercontrol 10, predictive processing unit 1 and local databases 2, 15, 16,30. Additionally, the preferred machine controlled or automated vehicleembodiment can also provide an indication of the historical and orpredicted traffic law enforcement profile, schedules and location tocomputer interface 12 or network interface 6 to provide an indication toanother system such as a vehicle integrated navigation system or avehicle integrated communication system. Additionally, the preferredmachine controlled or automated vehicle embodiment could optionallyutilize wired or wireless network interfaces 6, 19 which couldpreferably support G3, G4, G5 or other wireless data standards toprovide data communication with a server resource 3, 104 preferablyproviding traffic law enforcement citations and encounters from realtime database 5, historical database 4, traffic flow database 23, andmap database 17 and preferably server resource predictive processingunit 18. The automated vehicle 100 typically has a range of sensors 105,106, 107, 101 for determining location, lane position, positions ofother vehicles and objects, speed and direction. Sensors 105, 106, 107,101 could be a GPS, Lidar system, stereo vision system, camera system,machine vision system, Internet Protocol address locating system, radiolocation system, a Simultaneous localization and mapping (SLAM) system,dead reckoning system, radio location system, real-time locating system(RTLS), or an Inertial Navigation system or any combinations thereof.Sensors 105, 106, 107, 101 could interface to the enforcement profilerclient device 21, 102 through the vehicle sensor input 27 to provide thefunctions of the location determining unit 7, time determining unit 25,direction determining unit 26, velocity determining unit 28, Theenforcement profiler client apparatus 21, 102 may determine anycombinations of properties presented in Table 1 including for examplethe predicted enforcement profiles, estimated patrol schedules,estimated enforced speed limits, and estimated enforcement austerity inaddition to real time locations of traffic law enforcement from database5, 30 to provide vehicle control preferably including any combinationsof vehicle speed, direction, route, or route planning through thevehicle controller unit 24, 103 to improve travel efficiency anddecrease probability of citation, For instance, the enforcement profilerclient apparatus 21, 102 may control speed in conjunction with adaptivecruise control to ensure a vehicle does not violate enforcement profilessuch as enforced speed limit. Further, the enforcement profiler clientapparatus 21, 102 may also control speed to ensure a vehicle complieswith the posted or enforced speed limit at real time or predicted patrollocations. In addition, the enforcement profiler client apparatus 21,102 may also control the direction, route, and or route planning of avehicle to travel on road ways with the highest enforced speed limits orroadways with the fewest numbers of estimated patrol locations. Thoseskilled in the art will recognize that additional vehicle controlscenarios are possible and should be considered within the scope of thepresent invention.

As can be seen in a preferred embodiment of FIG. 1, the Client device 21of the Historic and Predictive Processing Unit (HPPU) 1 may accept inputfrom the User Control 10, location determining unit 7, time determiningunit 25, direction determining unit 26, velocity determining unit 28,vehicle sensor input 27, Weather Monitor 11, and database 2, 15, 16 and30. The User Control 10, provides a means for a user including a humanuser or a machine user such as a driver assisted, automated orautonomous vehicle to configure and control the client device 21. TheHistoric and Predictive Processing Unit 1 may provide annunciationoutput to the Audio Output 8 and Display 9 and also may have coupling toa computer interface 12 and or network interface 6 for interfacing to aserver resource 3 for uploading and downloading content from serverresource databases 4, 5, 17, 23 and or interacting with serverapplications running on server predictive processing unit 18.Additionally, the Historic and Predictive Processing Unit 1 may providevehicle control preferably including speed control, direction control,route control, route planning or any combinations thereof, through theVehicle Controller Unit 24. Additionally, a user of the client device 21may upload or add entries as crowd sourced records, to client databases2, 30 or to server databases 4, 5 using the client device networkinterface 6. Crowd sourced records could be shared with multipleinstances of client devices 21. Furthermore, vehicles equipped withsensors such as Lidar, stereo vision, cameras, or machine vision couldautonomously identify and report encounters with traffic law enforcementto the client device 21 through the vehicle sensor input 27 wherein theencounters would be added as crowd sourced records to client databases2, 30 or to server databases 4, 5 using the client device networkinterface 6. The network interface 6 may also enable multiple instancesof client devices 21 to communicate with a server resource 3 throughnetwork interface 19 which could be wired or wireless. Examples of theserver resource 3 could include a web server hosting the predictiveprocessing algorithms application executing on predictive processor 18which could be the server's central processing unit. In this preferredembodiment server resource 3 could serve web pages containing anycombination of real time, historic and or predicted traffic lawenforcement patterns, schedules, locations, enforcement profiles definedin Table 1, to client devices 21 or could also be a file serverproviding client device 21 access to databases 4, 5, 17, 23 which maypartially or totally eliminate the need for client device localdatabases 2, 16, 15, 30. Preferably a plurality of client devices 21could establish network interfaces 6 to a single instance of serverresource 3 through network interface 19, wherein the number of clientdevices 21 may be limited to less than 10,000,000; however, as thecapacity of server resource 3 increases, those skilled in the art wouldrecognize additional client devices 21 could communicate with a serverresource 3. Additionally, a network of client devices 21 could establishcommunications with each other through their respective networkinterface 6 preferably enabling multiple client devices 21 to sharerespective databases 2, 15, 16, 30 with other client devices 21preferably forming a distributed database and possibly eliminating theneed for server resource 3. Similarly, a plurality of server resources 3could establish network interfaces with each other through theirrespective network interfaces 19 preferably enabling multiple serverresources 3 to share respective databases 4, 5, 17, 23 with other serverresources 3 preferably forming a distributed database, wherein thenumber of server resources 3 may be limited to less than 10,000,000;however, those skilled in the art will recognize additional serverresources 3 could be included as network 19 capacities are increased.

The Location Determining Unit 7 may preferably provide the currentlocation, speed, direction of travel, date and time. The locationdetermining unit 7 could be realized using Global Positioning (GPS)technology, a GPS receiver, and it is well known that speed, directionof travel, date, and time can be derived from GPS data. Additionally,the location determining unit 7 could be a GPS, Lidar system, stereovision system, camera system, machine vision system, Internet Protocoladdress locating system, radio location system, a Simultaneouslocalization and mapping (SLAM) system, dead reckoning system, radiolocation system, real-time locating system (RTLS), or an InertialNavigation system or any combinations thereof. Other locationdetermining systems could also be used to implement the locationdetermining unit 7 and should be considered within the scope of thepresent invention. The time determining unit 25 could be a quartz basedreal time clock, network time protocol synchronized digital clock, or aGPS receiver that provides time and date. Other time determining systemscould also be used to implement the time determining unit 25 and shouldbe considered within the scope of the present invention. The velocitydetermining unit 28 could be a speedometer, GPS receiver thatdifferentiates location over time, or an inertial measuring unit thatdetermines speed. Other velocity determining systems could also be usedto implement the velocity determining unit 28 and should be consideredwithin the scope of the present invention. The direction determiningunit 26 could be a digital compass, GPS receiver that differentiateslocation over time, or an inertial measuring unit that determinesdirection. Other direction determining systems could also be used toimplement the direction determining unit 26 and should be consideredwithin the scope of the present invention. Utilizing the currentposition, time of day, speed and direction of travel provided by theLocation Determining Unit 7 or the time determining unit 25, velocitydetermining unit 28, direction determining unit 26, the HPPU 1, 18 mayaccess the historical database of traffic law enforcement citation andencounter records 2, 4 and real time database 5, 30 of current trafficlaw enforcement location, citation and encounter records, andstatistically profile and predict any combination of properties in Table1 which includes the locations, schedules and enforcement profiles oftraffic law enforcement patrols. Alternatively or additionally, thehistorical databases 2, 4 and real time database 5, 30 may contain anycombination of previously computed properties in Table 1 which includecombinations of predicted locations, schedules and enforcement profilesof traffic law enforcement patrols, collectively referred to aspredictive record, in which case the HPPU 1, 18 may access the database2, 4, 5, 30 to retrieve the predicted locations, schedules, andenforcement profiles of traffic law enforcement. Additionally, the HPPU1 can provide a visual representation of the historical and or predictedtraffic law enforcement profile, schedules and locations to the display9 and can also provide an acoustic representation of said historical andor predicted traffic law enforcement profile, schedules, and locationinformation to the Audio Output 8. HPPU 1 can also provide a visualrepresentation of the historical and or predicted traffic lawenforcement profile to the display 9 combined with a map of the roadwaypreferably provided by client device database 15 and or server resourcedatabase 17. The HPPU 1 can also provide an indication of the historicaland or predicted traffic law enforcement profile, schedules and locationto computer interface 12 or network interface 6 to provide an indicationto another system such as a vehicle integrated navigation system or avehicle integrated communication system. The HPPU 1 can also utilize thevehicle controller unit 24 to control the vehicle preferably includingany combinations of vehicle speed, direction, route, or route planning.

The HPPU 1, 18 preferably can also utilize traffic flow rates providedby client device traffic flow database 16 and or server resource trafficflow database 23 at a given location to determine traffic velocity anddensity as a function of time and location. Traffic flow databases 16and 23 preferably contain historic and real time measured traffic volumeand or velocity at given locations and associated times. Utilizinghistoric and real time an historic traffic volume and velocity fromdatabases 16 and or 23 and real time and historic encounter databases 2and or 4, the HPPU 1, 18 can determine combinations of traffic lawenforcement austerity properties presented in Table 1.

More specifically, the HPPU 1, 18 can preferably determine theEnforcement Leniency Profile of Table 1 which provides an indication oftraffic law enforcement patterns for issuing citation warnings versescitations. The HPPU 1, 18 may determine the Enforcement Leniency Profilefor locations and time intervals of interest by utilizing databasecitation records 2, 4, 5, 30 to determine the ratio or magnitude ofcitation warnings verses citations at locations and time frames as afunction of violation type. The Enforcement Leniency Profile provides anindication of how rigidly different types of violations are enforced.Additionally, the HPPU 1, 18 can preferably determine the Speed LimitEnforcement Austerity Relative to Average Traffic Flow Velocity also inTable 1 which provides a measure of the enforced speed limit relative tothe average flow velocity of traffic. Speed Limit Enforcement AusterityRelative to Average Traffic Flow Velocity can be determined by the HPPU1, 18 by determining the difference between traffic flow velocity fromreal time and historic traffic flow database 16, and 23 to traffic lawenforcement issued citation velocities from real time and historiccitation and encounter databases 2, 4, 5, 30 at given locations andtimes.

The HPPU 1, 18 can determine the Location Enforcement Austerity Relativeto Average Traffic Volume of Table 1, which quantifies the patrollocation patterns as a function of traffic volume and provides anindication as to which locations are patrolled more frequently underspecific traffic volume conditions and times. The HPPU 1, 18 candetermine the Location Enforcement Austerity Relative to Average TrafficVolume by utilizing the historic traffic speed and density 16, 23 todetermine the probability of a vehicle having an encounter with trafficlaw enforcement at a given time and location and the probability at eachlocation could provide a relative indication of the location patrolintensity or equivalently the relative amount a location is patrolled.Alternatively, if historic traftic speed and density 16,23 are notutilized, the location patrol intensity could be approximated as thelocation citation density which quantifies the number of citationsissued within a location and time interval. The location citationdensity may be interpreted relative to other locations within the regionto provide an indication of relative location patrol intensity.Additionally, the HPPU 1, 18 can preferably determine the Speed LimitEnforcement Austerity Relative to Average Traffic Flow Volume, presentedin Table 1, which provides a measure of the enforced speed limitrelative to the average flow volume of traffic. Speed Limit EnforcementAusterity Relative to Average Traffic Flow Volume can be determined at alocation by the HPPU 1, 18 by determining the historic estimatedenforced speed limit from databases 2,4,5,30 as presented in Table 1, atthe location as a function of traffic flow volume provided by database16, 23, and then inferring the enforced speed limit as the historicestimated enforced speed limit at said location with similar trafficflow volume.

Alternatively or additionally, combinations of the predicted traffic lawenforcement patrol schedules, locations and enforcement profiles,including enforced speed limits and enforcement austeritycharacteristics can be previously computed and stored in databases 2, 4,5, 30 at said given locations and times and the HPPU 1, 18 may accessthe database 2, 4, 5, 30 to retrieve the predicted traffic lawenforcement patrol schedules, locations and enforcement profiles,including enforcement austerity and enforced speed limit andcombinations thereof. Additionally, the HPPU 1 can provide a visualrepresentation of combinations of the historical and or predicted patrollocations, schedules, enforcement profiles, and austerity of traffic lawenforcement to the display 9 and can also provide an acousticrepresentation of said historical and or predicted locations, schedules,enforcement profiles, and austerity to the Audio Output 8. HPPU 1 canalso provide a visual representation of the historical and or predictedtraffic law enforcement patrol locations, schedules, enforcementprofiles, austerity and combinations thereof, to the display 9 combinedwith a map of the roadway preferably provided by client device database15 and or server resource database 17. Further the HPPU 1 can use realtime, historical and or predicted traffic law enforcement patrollocations, schedules, enforcement profiles, austerity and combinationsthereof, and the vehicle controller unit 24 to control a vehiclepreferably including any combinations of vehicle speed, direction,route, or route planning. For instance, the HPPU 1 may control speed inconjunction with adaptive cruise control to ensure a vehicle does notviolate enforcement profiles such as enforced speed limit. Further, theHPPU 1 may also control speed to ensure a vehicle complies with theposted or enforced speed limit at real time or predicted patrollocations. In addition, the HPPU 1 may also control the direction and orroute of a vehicle to travel on road ways with the highest enforcedspeed limits or roadways with the fewest numbers of estimated patrollocations. Those skilled in the art will recognize that the HPPU 1 couldutilize artificial intelligence algorithms and machine learningalgorithms to process real time, historical and or predicted traffic lawenforcement patrol locations, schedules, enforcement profiles, austerityand combinations thereof to provide vehicle control through the vehiclecontroller unit 24 to improve travel efficiency and decrease probabilityof citation, and additional vehicle control scenarios should beconsidered within the scope of the present invention.

Databases 2, 4, 5, 30 may contain combinations of previously computedpredicted locations, schedules and enforcement profiles of traffic lawenforcement, historical records and real time records derived fromcitations which were issued by traffic law enforcement agencies and orcrowd sourced encounters with traffic law enforcement collectivelyreferred to as citations or encounters in the present invention. Recordsderived from issued traffic law enforcement citations are typicallyconsidered more reliable than crowd sourced records and generally publicinformation and are preferably compiled and maintained by lawenforcement and government agencies and said agencies preferably includebut are not limited to State Highway Patrols, City and County Policeagencies, Department of Motor Vehicles and Municipal Courts. Data bases2, 4, 5, 30 could also contain crowd sourced citations or encounterswith traffic law enforcement contributed by individuals. Databases 2, 4,5, 30 could contain an entry for each issued citation or encounter butcould be in various compressed formats to improve storage efficiency.Each entry of databases 2, 4, 5, 30 preferably may contain a combinationof the fields similar to those presented in Equation 1 for the recordformat; however, a subset of said fields may be available per record oradditional fields such as vehicle type, or color could be present ineach record. Additionally, different means could be used to compress thedatabases 2, 4, 5, 30 entries to improve storage efficiency. Equation 1,demonstrates a preferred content representation of each record indatabase 2, 4, 5, 30 derived from citation records.record_entry={location, time, date, direction, type, violation_speed}

Equation 1. Preferred Citation and Encounter Record Properties andMembers of Database

Equation 1 record properties may also be referred to as members orevents of said record. Said record member: location is preferably thelocation where the violation was observed and the citation issued. Saidlocation field is preferably in latitude and longitude units; however,it could additionally be reported by but not limited to mile postmarker, crossroads location, or street address. Said record member: timeand date are preferably the time and date at which the encounteroccurred or citation was issued, time could include both the time anddate. Said record member: direction is preferably the direction oftravel at which time the encounter or citation was issued. Said recordmember: type is preferably the type of encounter or citation which mayinclude speeding, expired registration and other infraction types orcould be the reason for traffic law enforcement stopping vehicle. Saidrecord member: speed preferably represents the speed of the vehicle whena speeding citation was issued or could also represent the velocitywhich would specify both the speed and direction. Crowd sourced recordsof encounters with traffic law enforcement can also be stored indatabases 2, 4, 5, 30 in a representative format of Equation 1, whereincrowd sourced data may be a sighting encounter of traffic lawenforcement such that possibly only location, time and direction mightbe present in the record entry Equation 1.

Furthermore, in another preferred embodiment, databases 2, 4, 5, 30could contain predicted traffic law enforcement properties and patternsat locations and times, derived from processed citation records similarto Equation 1, preferably including a combination of the propertiesdefined in Table 1, and represented in Equation 1a. Those skilled in theart will recognize that additional enforcement characteristics andpredicted profile properties could be derived from traffic lawenforcement citation records and crowd sourced encounters with trafficlaw enforcement and stored in databases 2, 4, 5, 30 and should beconsidered within the scope of the present invention. Equation 1a,demonstrates a preferred format representation of each entry of database2, 4, 5, 30 containing predictive entries. Saidpredictive_record_entries of Equation 1a preferably contain anycombination of traffic law enforcement properties presented in Table 1.Predictive_record_entry={location, patrol schedule, patrol location,location patrol intensity, enforcement profile}

Equation 1a. A Preferred Post Processed Predictive Database RecordFormat.

Reference Table 1 for definition of predicted traffic law enforcementproperties and patterns.

Most state and city departments of transportation monitor and reporthistorical and current traffic flow volume and rate information forroadways. Client device 21 preferably incorporates a traffic flow database 16 which preferably contains records of historic traffic flowvolumes and or historic traffic velocities at locations verses time.Additionally, server resource 3 may contain a traffic flow and velocitydatabase 23. Databases 16 and 23 may contain real time and or historictraffic flow records. The HPPU 1, 18 can preferably utilize the trafficrate and volume from databases 16, 23 to determine the probability of avehicle having an encounter with traffic law enforcement at a given timeand location and the probability at each location preferably provides arelative indication of patrol locations. Additionally, HPPU 1, 18 canpreferably determine the enforced speed limit relative to the rate andflow of traffic by comparing the traffic speed from databases 16, 23 totraffic law enforcement issued citation speeds from databases 2, 4, 5,30 at given locations and times. Preferably HPPU 1, 18 can compare thetraffic flow rate with speeding citation issued speeds and determine theestimated speed at which traffic law enforcement issues citations atgiven times and locations relative to the average traffic rate and ormay determine the austerity with which traffic law enforcement enforcesthe speed limit relative to average traffic rates and volumes at givenlocations and times. Equation 2 demonstrates a preferred traffic flowentry format of databases 16, 23.traffic_flow_entry={location, volume, velocity, time}

Equation 2. A Preferred Traffic Flow Entry Format

Said location field of Equation. 2 is preferably in latitude andlongitude coordinate units and the volume preferably represents thenumber of vehicles per second travelling through the location at saidtime and with the average said velocity.

In a preferred embodiment, the HPPU 1, 18 utilizes traffic flow and ratehistorical and real time records 16, 23 to enable improved prediction oftraffic law enforcement speed traps, speed cameras and or patrollocations and or austerity of enforced speed limits relative to thenatural flow of traffic. More specifically, in a preferred embodimentthe HPPU 1, 18 may determine the relative intensity of patrols at givenlocations as the relative ratio of issued traffic citations to trafficvolume at said given locations. In a preferred embodiment the HPPU 1, 18may determine the location of estimated speed traps and speed cameras aslocations with relative ratios of issued traffic citations to trafficvolume that exceed a threshold relative to the ratios of issued trafficcitations to traffic volume in surrounding areas. Furthermore, in apreferred embodiment the HPPU 1, 18 may compute the schedule of speedtraps, speed cameras, and or the schedule of patrol locations bydetermining said ratios of issued traffic citations to traffic volume athistoric times and locations and then cross correlating said ratios as afunction of time to identify patrol schedules which are time correlated.Those skilled in the art will realize the austerity of enforced speedlimits at corresponding locations and the locations of predicted speedtraps could be computed antecedently and contained within databases 2,4, 5, 30 and the HPPU 1, 18 could retrieve austerity of enforced speedlimits and locations of predicted speed traps and speed cameras fromdatabases 2, 4, 5, 30.

Additionally, in a preferred embodiment HPPU 1, 18 may utilize thedifference between the traffic flow average velocity from database 16,23 at locations and the velocities at which citations have been issuedat said locations from databases 2, 4, 5, 30 and determine an indicationof speed limit enforcement profile and austerity relative to trafficflow velocities. Furthermore, in a preferred embodiment the HPPU 1, 18may estimate the schedule of speed limit austerity relative to trafficflow velocities by determining the difference between the historictraffic flow average velocity 16, 23 at locations and times and thevelocities at which historic citations have been issue at correspondinglocations and times from database 2, 4, 5, 30. Said differences are thencross correlated as a function of time to identify speed limit trafficlaw enforcement austerity at corresponding locations which are timecorrelated and thus estimate future speed limit enforcement profileausterity. Those skilled in the art will realize the austerity ofenforced speed limits relative to traffic flow average velocities andvolumes at corresponding locations could be computed antecedently andcontained within databases 2, 4, 5, 30 and the HPPU 1, 18 could retrieveausterity of enforced speed limits relative to traffic flow rates fromdatabases 2, 4, 5, 30.

In a preferred embodiment the HPPU 1, 18 can determine an optimaldriving route to maximize travel speed and minimize the probability ofreceiving a citation by utilizing the ratio of traffic volume 16, 23verses traffic citation density 2, 4, 5 at locations along optionalroutes to determine an optimal route which maximizes the said ratio andminimizes the travel travel time. Further, the HPPU 1, 18 can utilizethe estimated enforced speed limits, patrol schedules, patrol locations,enforcement profiles from databases 2, 30, 4, 5 and roadway mapdatabases 15, 17, and preferably the time 25 to determine an optimaldriving route between starting and ending locations to minimize traveltime and minimize probability of receiving a citation.

Traffic law enforcement encounters and citations may be reported basedon the milepost marker or address thus, databases 2, 4 preferably couldcontain entries for mapping Mile Post Marker to latitude and longitudecoordinates, and street address latitude and longitude coordinates.coordinate=(location by mile post, street address, lat-long)

Equation 3. Mile Post, Address to Latitude and Longitude Example EntryFormat

FIG. 2 shows a preferred method 212, for profiling traffic lawenforcement. The objectives of method 212 may include combinations ofpredicting the likely traffic law enforcement patrol locations,schedules, enforcement profiles, speed trap locations, red light cameralocations, speed camera locations, enforcement austerity, enforcementhistograms, maximum driving speed to avoid citation and are summarizedin Table 1. Further objectives of method 212 may include providinghistorical and real time records of traffic patrol and trafficenforcement citations and encounters. Further objectives of method 212may include utilizing traffic law enforcement patrol locations,schedules, and enforcement profiles in Table 1 to control a vehiclesspeed, direction, route, or any combinations thereof to improve travelefficiency while decreasing the probability of citation. Method 212could be implemented by a preferred embodiment of the traffic lawenforcement profiler apparatus 14. More specifically, in a preferredembodiment, method 212 could be implemented on the client device 21 orin combination with server resource 3 or in combination with a pluralityof client devices 21 or a plurality of server resources 3. Saidplurality of client devices 21 and plurality of server resources 3 couldbe limited to 10 million, but those skilled in the art will recognizethat the limitations of the plurality of client devices 21 and serverresources 3 could utilize the cellular networks, wireless data networks,internet or other broadband network which could scale to support greaterthan 10 million devices 21 and servers 3.

In the preferred method 212, context operation 201 may determine currentlocation coordinates, time, date, direction and speed 225 andcombinations thereof from the location determining unit 7. Operation 202preferably determines the geographical region and historic time periodof interest which preferably results in location coordinates definingthe boundaries of the region and historic time intervals 223, saidboundaries are preferably in the range of 100 feet to 5000 miles. Saidhistoric time intervals preferably may extend 20 years into the pastrelative to current time; however, those skilled in the art willrecognize the historic time intervals 223 could extent to the durationfor which records were maintained and should be considered within thescope of the present invention. Furthermore, the historic time intervals223 could actually extend into the future relative to current timepreferably 20 years for retrieving records which may include estimatedand predicted patrol schedules, patrol locations, enforcement profiles,and properties presented in Table 1, and combinations thereof.

The database retrieve operation 203, preferably utilizes the regions andtimes of interest 223 to access the historical databases 2, 4 and realtime database 5, 30 to retrieve a plurality of records derived fromtraffic law enforcement citations and encounters, and records ofestimated and predicted patrol schedules, patrol locations, andenforcement profiles and combinations thereof 222. The plurality ofrecords 222 is preferably limited to the records in the region and timeinterval of interest 223 and is typically less than 100 million recordsbut those skilled in the art will recognize as the length historicalrecords databases increase and the region of interest increases, thenumber of records may also increase and should be considered within thescope of the present invention. The plurality of records 222 may consistof record entries similar to Equation 1, Equation 1a, and Table 1 andany combinations of the individual subfields thereof, referred to asdatabase entry (dbe). Other options for storing the content or dberecords in historical databases 2, 4 and 5, 30 are possible which may bemore efficient and should be considered within the scope of the present.Additionally or alternatively, databases 2, 4, 5, 30 may store dberecords which may be predicted traffic law enforcement property fieldsin Table 1 and combinations thereof. Storing predicted enforcementprofiles, locations and schedules of traffic law enforcement providesthe benefits of reduced processing requirements of the HPPU 1, 18 andreduced database 2, 4, 5, 30 sizes. Additionally, in a preferredembodiment the database retrieve operation 203 may retrieve a pluralityof historic traffic flow volume and velocity database 16, 23 records androadway map database 15,17 entries, for the region and timeframe ofinterest 223, and provide on 222.

In a preferred embodiment, predicted traffic law enforcement patrollocations can be determined by the patrol location estimator operation204, predicted traffic law enforcement schedules can be determined bythe patrol schedule estimator operation 205, and estimated enforcementprofile can be determined by the patrol profile estimator operation 206by utilizing the dbe 222, and optionally the traffic flow and mapdatabase entries 222.

In a preferred embodiment, operations 204, 205, 206 may all be present;however, in other preferred embodiments any combination or subset ofoperations 204, 205 and 206 may be present. Operations 204, 205, 206preferably process the plurality of historical and real time data baseentries 222 provided from databases 2,4,5,30,16,23 to producestatistical estimates of past and present patrol locations in operation204, estimates of schedules in operation 205 and enforcement profiles inoperation 206, and time extrapolate statistical estimates to producepredicted patrol locations 215 in operation 204, patrol schedules 213 inoperation 205 and enforcement profiles 214 in operation 206.

The Indication operation 207 may accept and present any combination ofthe predicted patrol schedules 213, locations 215, profiles 214,database records 222, and current location, date, time and velocity 225of apparatus 14. Additionally, Indication operation 207 may enunciate anaudio or visual indication or alarm if apparatus 14 location andvelocity 225 is approaching predicted patrol locations 215 or is withinpredicted patrol schedules 213 with historical patrol location orhistorical patrol schedule correlation. Additionally, Indicationoperation 207 may enunciate an audio or visual indication or alarm ifthe apparatus 14 location, time and velocity 225 are approaching alocation with a statistically significant chance of encountering trafficlaw enforcement as indicated by the predicted patrol location 215 andwith a predicted patrol schedule 213 and combinations thereof.

The vehicle control operation 226 may accept any combinations of thepredicted patrol schedules 213, locations 215, enforcement profiles 214,database records 222, and current location, date, time, direction andvelocity 225 of apparatus 14. Additionally, the vehicle controloperation 226 may control a vehicles speed, direction, route, or anycombinations thereof to improve travel efficiency while decreasing theprobability of citation. More specifically, the vehicle controloperation 226 may control speed in conjunction with adaptive cruisecontrol to ensure a vehicle does not violate enforcement profiles suchas enforced speed limit at a given location. The vehicle controloperation 226 may also control speed to ensure a vehicle complies withthe posted or enforced speed limit at real time or predicted patrollocations. The vehicle control operation 226 may also control thedirection and or route of a vehicle to travel on road ways with thehighest enforced speed limits or roadways with the fewest numbers ofestimated patrol locations to reduce travel time. Those skilled in theart will recognize that the vehicle control operation 226 could utilizeartificial intelligence algorithms and machine learning algorithms toprocess patrol schedules 213, locations 215, enforcement profiles 214,database records 222, and combinations thereof to provide vehiclecontrol to improve travel efficiency and decrease probability ofcitation, and additional vehicle control scenarios should be consideredwithin the scope of the present invention.

In another preferred embodiment, operation 203 may provide records ofhistoric citation and encounter records to operation 207, over theregion of interest 223, which may present the historical citationinformation 222. Additionally, any combination of record fields ofEquation 1, 1a or Table 1 maybe be presented by operation 207. In apreferred embodiment the traffic enforcement profiler method 212provides historic traffic law enforcement data 222. From the presentedhistoric data 222, a user could preferably interpret, and or estimatepatrol locations, schedules and enforcement profile including enforcedspeed limits. Those skilled in the art will recognize the historicrecords 222 could further be filtered by a user and the properties therecords could be visually encoded to communicate the enforcement profileand should be considered within the scope of the present invention.

Estimating Patrol Locations, Schedules, and Enforcement Profiles ofTraffic Law Enforcement

In a preferred embodiment of the present invention, apparatus 14 mayutilize the principles of statistical analysis and probability theory inestimating the properties of traffic law enforcement presented inTable 1. In particular, traffic law enforcement properties of Table 1may be modeled as a stochastic process and the predicted patrollocations, schedules and enforcement profiles could be considered randomvariables. Records derived from historic traffic law enforcementcitations and encounters can be considered events of the underlyingstochastic process and utilized to estimate the probability distributionof patrol locations, schedules, patrol profiles and the propertiespresented in Table 1 to provide a historical characterization of trafficlaw enforcement properties as a function of time and location.Additionally, the historic records of traffic law enforcement citationsand encounters may be utilized to probabilistically predict thelocations, schedules, patrol profiles, and properties of Table 1 oftraffic law enforcement.

In a preferred embodiment, properties including those of Equation 1,derived from issued citations and encounters, which can be consideredevents of the traffic law enforcement stochastic process, may becorrelated to estimate the statistical relationship and dependenciesbetween said properties and enable predicting current or future eventsof traffic law enforcement which may include patrol locations, schedulesand enforcement profiles. Correlation refers to any statisticalrelationship between two or more random variables or sets of events froma stochastic process. Equation 4a presents a general algorithmimplemented by a preferred embodiment of apparatus 14. Equation 4a maydetermine the correlation between a plurality of random variables X₁,X₂, X_(N), wherein σ_(N). represents the mean of the associated randomvariable and σ_(N) represents the variance of the associated randomvariable. E is the expected value operator. Random variables X₁, X₂,X_(N) of traffic law enforcement may include patrol locations,schedules, enforcement profiles and properties of presented in Table 1and Equation 4a, may determine the correlation and hence statisticalpredictability of patrol locations, schedules, and enforcement profiles.

$\begin{matrix}{{\rho_{X_{1},{X_{2}\ldots\; X_{n}}} = {{{corr}\left( {X_{1},{X_{2}\mspace{11mu}\ldots\mspace{11mu} X_{N}}} \right)} = \frac{E\left\lbrack {\left( {X_{1} - \mu_{X_{1}}} \right)\left( {X_{2} - \mu_{X_{2}}} \right)\mspace{11mu}\ldots\mspace{11mu}\left( {X_{N} - \mu_{X_{N}}} \right)} \right\rbrack}{\sigma_{X_{1}}\sigma_{X_{2}}\mspace{11mu}\ldots\mspace{11mu}\sigma_{X_{N}}}}}{A\mspace{14mu}{preferred}\mspace{14mu}{multi}\text{-}{variable}\mspace{14mu}{correlation}\mspace{14mu}{method}\mspace{14mu}{to}\mspace{14mu}{predict}\mspace{14mu}{events}\mspace{14mu}{of}\mspace{14mu}{traffic}\mspace{14mu}{law}\mspace{14mu}{{enforcement}.}}} & {{Equation}\mspace{14mu} 4a}\end{matrix}$

Equation 4a1 may utilize a plurality of observed events C₁, C₂, C_(N) ofrandom variables X₁, X₂, X_(N) to determine the sample correlation whichapproximates the correlation of Equation 4a, which may statisticallypredict patrol locations, schedules and enforcement profiles.

$\begin{matrix}{{{{\rho_{X_{1},{X_{2}\ldots\; X_{n}}} \approx {r_{C_{1}C_{2}C_{N}}\left( {\tau_{1}\mspace{11mu}\ldots\mspace{11mu}\tau_{N}} \right)}} = {\int_{\varphi}{{C_{1}(\varphi)}{C_{2}\left( {\tau_{1} + \varphi} \right)}{C_{N}\left( {\tau_{1} + \tau_{N} + \varphi} \right)}d\;\varphi}}}{A\mspace{14mu}{preferred}\mspace{14mu}{multi}\text{-}{variable}\mspace{14mu}{sample}\mspace{14mu}{correlation}\mspace{14mu}{method}\mspace{14mu}{to}\mspace{14mu}{predict}\mspace{14mu}{events}\mspace{14mu}{of}\mspace{14mu}{traffic}\mspace{14mu}{law}\mspace{14mu}{{enforcement}.}}}\;} & {{Equation}\mspace{14mu} 4{a1}}\end{matrix}$

Equation 4a1 is presented in continuous integral form; however, discreteversions of Equation 4a1 are also possible and should be consideredwithin the scope of the present invention. In Equation 4a1, theplurality of observed events C₁, C₂, C_(N) could be derived fromencounters and citations of traffic law enforcement 222 and theproperties associated with each citation as presented in Equation 1. Theφ operator may represent the variable to perform correlation over andcould be any combination of time, location, citation speed, citationtype or any combination of properties of Equation 1.

₁,

_(N) may be considered the plurality of functions for which thecorrelation is computed and could also be any combination of time,location, citation speed, and any combination of properties ofEquation 1. Relative maximum in the correlation result Equation 4a1, maybe identified as periodic patrol properties as a function of

₁,

_(N) and could be time extrapolated into the present or future topredict events of traffic law enforcement including patrol locations,schedules, enforcement profiles and the properties provided in Table 1.

Statistical analysis of the properties of event records, derived fromissued citations and encounters of traffic law enforcement, preferablyincluding the properties of Equation 1, can be utilized forcharacterizing the properties of the traffic law enforcement stochasticprocess. More specifically, descriptive statistics principles can beutilized to quantitatively describe the historical characteristics oftraffic law enforcement from the event records which may includedetermining the mean, variance, frequency, histogram, distributions,accumulations, minimum and maximum of properties derived from historicalcitation records including those of Equation 1. Further, statisticalinference can be utilized to infer and predict the properties presentedin Table 1, of traffic law enforcement, from historical encounters withand citation records of traffic law enforcement, which may includepatrol locations, schedules, and enforcement profiles. Correlation isalso a statistical measure which refers to a broad class of statisticalrelationships involving dependence between random variables. Thefollowing sections present preferred algorithms for utilizingcorrelation and statistical analysis to estimate patrol locations,schedules, and enforcement profiles of traffic law enforcement and mayinclude the properties presented in Table 1.

Patrol Location Estimation—Operation 204

The patrol location estimator 204 preferably determines the locationswhere it is likely to encounter traffic law enforcement patrols. Table 1provides a listing of estimated traffic law enforcement properties whichmay be provided by the current invention, and the estimation of PatrolLocation and the Patrol Location Intensity properties of Table 1 may bedetermined by the Patrol Location Estimator operation 204. In apreferred embodiment, the patrol location estimator 204, may implementalgorithms utilizing historic and or real time records similar toEquation 1, derived from traffic law enforcement citations and crowdsourced data 222 to predict patrol locations 215. In yet anotherpreferred embodiment, the patrol location estimator 204 may utilizerecords similar to Equation 1a, of previously determined predictedpatrol locations and location patrol intensity of traffic lawenforcement patrols 222, to predict patrol locations and location patrolintensities 215.

A preferred method of the patrol location estimator 204 to determinepatrol location estimates 215, calculates the correlation between aplurality of records 222 and identifies traffic law enforcement patrollocations. In a preferred embodiment of patrol location estimationoperation 204, Equation 4a, 4a1 could be utilized to compute thelocation correlation between citation record 222 locations from Equation1, possibly as a function of citation type, time, and direction.Relative maximum in the correlation result may be identified by thelocation estimation operation 204 as periodic patrol patterns at alocation and can be time extrapolated into the present or future andhence predict patrol locations 215. In a preferred embodiment, thepatrol location estimator 204 correlates a plurality of random citationevents C₁, C₂, C_(N) to optimize the accuracy of patrol locationestimates 215; however, preferably said plurality of events to becorrelated could include any combination of the following enumeratedrecords 222 of Equation 1:

-   -   1) Citations issued at a location    -   2) Citations issued at a location and in a direction    -   3) Citations issued as a function of time at a location    -   4) Citations issued as a function of time at a location and in a        direction    -   5) Traffic flow volume as a function of time at a location    -   6) Traffic flow rate as a function of time at a location

In another preferred embodiment, the patrol location estimator 204 cancompute the auto correlation of the time sequence of citation records222 at a plurality of locations limited to be within the region ofinterest 223 wherein the relative maxima of said auto correlationindicates the presence of periodic patrol patterns at said plurality oflocations. Operation 204 can observe the relative maxima of saidautocorrelation and determine locations where patrols occur and theperiodic times at which said patrols occur and time extrapolate theperiodic times of the patrols to predict locations of patrols at thepresent and or future times and provide said predicted patrol locationsand times as indication 215. Equation 4b provides a preferred method ofoperation 204 for computation of the auto correlation of issuedcitations locations as a function of citation type and time.

$\begin{matrix}{{{{coorelation}\mspace{11mu}\left( {{type},{loc},\tau} \right)} = {\int_{t = {t1}}^{t = {t\; 2}}{{{dbe}\left( {{type},{loc},\tau} \right)}{{dbe}\left( {{type},{loc},{\tau - t}} \right)}}}}{A\mspace{14mu}{preferred}\mspace{14mu}{correlation}\mspace{14mu}{method}\mspace{14mu}{to}\mspace{14mu}{estimate}\mspace{14mu}{patrol}\mspace{14mu}{{locations}.}}} & {{Equation}\mspace{14mu} 4b}\end{matrix}$

In equation 4b, the correlation can be computed for a citation type andor location and patrol interval time

, between a starting time t₁ and an ending time t₂. In a preferredembodiment, operation 204 can compute the correlation of equation 4b andidentify relative maximums as a function of patrol interval time

which indicate traffic law enforcement patrol locations and patrolintervals and provide said predicted patrol locations and times asindication 215.

In another preferred embodiment, the patrol location estimator 204 cancompute the accumulation of citation records 222 at a plurality oflocations between a time interval t₁ and t₂ and determine historicpatrol locations as locations with accumulation results above athreshold and or accumulation results which are relatively higher thanother locations. Operation 204 can predict current and future patrollocations from historic patrol locations and provide said predictedpatrol locations as indication 215.

In a preferred embodiment, the patrol location estimator 204 computesthe accumulation 215 of historical and or real time traffic lawenforcement encounters preferably as a function of time, location, andby type of citation or encounter type. The accumulation 215 at eachlocation may be interpreted relative to other locations to determinepatrol location estimates preferably as the locations with higherrelative accumulations. Alternatively or additionally, the patrollocation estimation operation 204 could preferably compare theaccumulation 215 at each location to a threshold to determine patrollocation estimates preferably as the locations with accumulationsgreater than the threshold. A preferred accumulation algorithm is shownin Equation 4; however, other algorithms to compute the citationlocation accumulation are possible and should be considered within thescope of the present invention. Equation 4 accumulation isalgorithmically equivalent to the auto correlation with time shift ofzero and computed at a plurality of locations.

$\begin{matrix}{\mspace{79mu}{{{{accumulation}\mspace{11mu}\left( {{type},{loc},t} \right)} = {\sum\limits_{t = {t\; 1}}^{t = {t\; 2}}{{dbe}\mspace{11mu}\left( {{type},{loc},t} \right)}}}{A\mspace{14mu}{preferred}\mspace{14mu}{computation}\mspace{14mu}{of}\mspace{14mu}{encounter}\mspace{14mu}{location}\mspace{14mu}{{accumulation}.}}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

The following terms of equation 4 are defined:

Accumulation(loc)—total occurrences of encounters at a given locationloc, time t, and of type

Loc—location, and may preferably include direction

dbe(type, loc, t)—data base entry at time t and location loc and oftype.

t—time

t1—start time and date of interval for calculating the total number ofencounters

t2—end time and date of interval for calculating the total number ofencounters

Equation 4 preferably computes the total number of occurrences ofcitations and or encounters issued at a given location loc within aspecified time period t1 to t2 referred to as citation accumulation andof a type of citation. Operation 204 preferably computes the citationlocation accumulation for each location 223 to produce a completehistogram of citation accumulations at each location loc and for eachcitation type. Preferably the time period t1 to t2 is large enough togive an accurate representation of issued citations at a given loc, saidperiod t1 to t2 being preferably in the range of 1 minute to severalyears.

In another preferred embodiment, the patrol location estimator 204 maycompute the patrol location estimates utilizing traffic flow databaseentries 16 and or 23 to compute the ratio of accumulated trafficencounters as a function of time, location and type to traffic volume asa function of time and location to normalize said accumulated trafficlaw enforcement encounters and preferably enable more accurateprediction of estimated patrol locations. Equation 4c demonstrates anexample algorithm for computing the citation accumulation normalized bytraffic volume.

$\begin{matrix}{{{{normalized\_ accumulation}\mspace{11mu}\left( {{type},{loc},t} \right)} = {{\left( {\sum\limits_{t = {t\; 1}}^{t = {t\; 2}}{{dbe}\mspace{14mu}\left( {{type},{loc},t} \right)}} \right)/{traffic\_ flow}}\mspace{11mu}\left( {{loc},t} \right)}}{A\mspace{14mu}{preferred}\mspace{14mu}{computation}\mspace{14mu}{of}\mspace{14mu}{normalized}\mspace{14mu}{encounter}\mspace{14mu}{location}\mspace{14mu}{{accumulation}.}}} & {{Equation}\mspace{14mu} 4c}\end{matrix}$

The following terms of equation 4 are defined:

Normalized Accumulation(type, loc, t)—total occurrences of encounters ata given location loc, time range t2 to t1, and of type

Traffic_flow(loc,t)—traffic volume at location loc and time interval t2to t1.

Loc—location and may preferably include direction

dbe(type, loc,t)—data base entry at time t and location loc of type

t—time

t1—start time and date of interval for calculating the total number ofcitations

t2—end time and date of interval for calculating the total number ofcitations

In yet another preferred embodiment, the patrol location estimator 204may utilize records similar to Equation 1a, of previously determinedpredicted locations of traffic law enforcement patrols 222, to predictpatrol locations 215.

The predicted patrol locations produced by the patrol location estimator204 are provided on indication 215.

Patrol Schedule Estimator Operation 205

In a preferred embodiment, the patrol schedule estimator 205 predictsthe schedules of traffic law enforcement patrols at a plurality oflocations within the region of interest 223. Table 1 provides a listingof estimated traffic law enforcement properties which may be provided bythe current invention, and the Estimated Patrol Schedule property ofTable 1 may be determined by the Patrol Schedule Estimator operation205. The patrol schedule estimator 205 could implement algorithmsutilizing historic and real time records derived from traffic lawenforcement citations and crowd sourced data 222 to predict patrolschedules. In another preferred embodiment, the patrol scheduleestimator 205 could compute the time-location correlation Equation 4a,4a1 of records similar to Equation 1, which are derived from historictraffic law enforcement citations and crowd sourced data 222, toestimate traffic law enforcement patrol schedules 213, preferably as afunction of location and by type of citation or encounter type. In yetanother preferred embodiment, the patrol schedule estimator 205 mayutilize records similar to Equation 1a, of previously predictedschedules of traffic law enforcement patrols 222, to predict traffic lawenforcement patrol schedules 213, at a plurality locations within theregion of interest 223.

In a preferred embodiment, the patrol schedule estimator 205 preferablycomputes the time-location correlation of encounters 213 retrieved fromdatabase 2, 4, 5, 30 and preferably uses the time-location correlationof historical encounters to predict patrol schedules at a plurality oflocations within the region of interest 223. The patrol scheduleestimator 205 could implement the correlation method of Equation 4a, 4a1and the autocorrelation method of Equation 4b to estimate patrolschedules. In equation 4b, the correlation can be computed as a functionof citation type and or location and patrol interval time

, between a starting time t₁ and an ending time t₂. In a preferredembodiment, the patrol schedule estimator operation 205 can compute thecorrelation of equation

4a1 or 4b and identify relative maximums as a function of patrolinterval time T which indicates traffic law enforcement patrol locationsand patrol intervals. From said patrol intervals

, the patrol schedule estimator operation 205 can infer the historicschedules of traffic law enforcement and time extrapolate said historicschedules to predict current and future schedules of traffic lawenforcement and provide said predicted patrol locations and associatedpatrol schedules as indication 213.

FIG. 3 graphically demonstrates a preferred correlation method of patrolschedule estimator 205 for estimating traffic law enforcement patrolschedules and locations. FIG. 3 shows a representative plot of theaccumulation of traffic law enforcement encounters 700 at a location asa function of time and demonstrates a preferred method of correlation toderive patrol schedule estimates 720 and time extrapolation to infer andpredict patrol schedules at present and future times 704. Time instanceswhere there is an increase in law enforcement encounters are shown asencounter accumulation maximums 710, 711, 712, 713, 714 and indicatepossible times where there may be increased patrols. Graph 720 maypresent the result of correlating the encounter accumulation 700 as afunction of time which yields the encounter correlation result 720 withmaxima 723, 724, 725, 726, 727 indicating the presence of periodicpatrol patterns in historic encounter accumulations and provides anestimated patrol schedule. The amplitude of correlation 720 provides anindication of reliability of the predicted patrol schedule at saidlocation and time, while the width of the correlation maxima723,724,725, 726, 727 provides an indication of the time variance andduration of patrol interval. From the correlation maxima 723,724, 725,726, 727 predicted patrol schedules are provided and can be timeextrapolated to create estimated current and future patrol schedules 704at said location. The maxima 706, 703, 707, 708 of the predicted patrolschedules 704 provide specific statistically probable patrol times andthe amplitude of the maxima 706, 703, 707, 708 provide a relativeindication of the likelihood of patrol and the width of said maximaprovides an indication of the typical duration of the patrols or timeuncertainty of the patrols at said location.

In a preferred embodiment, correlation of historic traffic lawenforcement records could be performed over a period of minutes, hours,days, weeks or even years to provide estimates of traffic lawenforcement patrol schedules. However, correlation of historic trafficlaw enforcement records over time intervals of 10 years or less isgenerally sufficient to produce reliable estimates of patrol schedules213. The correlation results from historical law enforcement encounterscan be time extrapolated to infer real time and future patrol schedules213.

Conceptually various algorithms and equations can be employed by thepatrol schedule estimator 205 to estimate patrol schedules from trafficlaw enforcement citation records and crowd encounter 222 and should beconsidered within the scope of the present invention. Additionalpreferred algorithms for estimating patrol schedules by computing thetime-location correlation are shown in equation 5 and equation 5a.Equation 5a presents a preferred method of the patrol schedule estimatoroperation 205 for correlating records and encounters 222 as a functionof location, patrol intervals and over a time period interest. Equation5a may compute the encounter_correlation Equation 5a by auto correlatingthe database records 222 at a location and for a patrol time interval.The relative maximums of the encounter_correlation Equation 5a,correspond to periodic patrol patterns or schedules of traffic lawenforcement of interval t_patrol at the associated location loc. In apreferred embodiment, the patrol schedule estimator operation 205 canobserve said relative maximums of the encounter_correlation Equation 5a,and determines the periodic traffic law enforcement patrol intervals.The patrol schedule estimator 205 may time extrapolate said patrolintervals to predict the current and or future enforcement schedules atthe associated locations and provides said predicted patrol locationsand associated schedules as indication 213.

$\begin{matrix}{{{{encounter\_ correlation}\mspace{11mu}\left( {{{loc} \pm {\Delta\alpha}},{t\_ patrol}} \right)} = {\int_{t = {start}}^{t = {end}}{{{dbe}\left( {{{loc} \pm {\Delta\alpha}},{t \pm {\Delta ɛ}}} \right)}{{dbe}\left( {{{loc} \pm {\Delta\alpha}},{{t \pm {\Delta ɛ}} - {t\_ patrol}}} \right)}}}}{A\mspace{14mu}{preferred}\mspace{14mu}{algorithm}\mspace{14mu}{for}\mspace{14mu}{computation}\mspace{14mu}{of}\mspace{14mu}{encounter}\mspace{14mu}{time}\text{-}{location}\mspace{14mu}{{correlation}.}}} & {{Equation}\mspace{14mu} 5a}\end{matrix}$

The following preferable terms of equation 5a are defined:

correlation(loc, t_patrol)—the correlation of encounters at location locand patrol interval t_patrol

Loc+/−Δα—location of said violation within +/−Δα distance, Δα preferablyranges from 100 feet to 50 miles. Loc preferably also includesdirection.

dbe(loc+/−Δα, t+/−Δε+/−t_patrol)—represents a database entry which has amatching location loc and issue time Δt+/−Δε.

tstart—the earliest time for calculating correlation time duration.

tend—the last time for calculating correlation time duration.

t+/−Δε—time of said violation within +/−Δε time, Δε preferably rangesfrom 1 minute to 1 day.

Equation 5, is yet another preferred algorithm for estimating patrolschedules by correlating the database entries as a function of time andlocation.

$\begin{matrix}{{{{encounter\_ correlation}\mspace{11mu}\left( {{{loc} + {\Delta\alpha}},{\Delta t}} \right)} = {\sum\limits_{n = {nmin}}^{nmax}{{dbe}\mspace{11mu}\left( {{{loc} + {\Delta\alpha}},{{n\;\Delta\; t} + {\Delta ɛ}}} \right)}}}{{Preferred}\mspace{14mu}{algorithm}\mspace{14mu}{for}\mspace{14mu}{computation}\mspace{14mu}{of}\mspace{14mu}{encounter}\mspace{14mu}{location}\text{-}{time}\mspace{14mu}{{correlation}.}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

The following preferable terms of equation 5 are defined:

correlation(loc, Δt)—the total correlation of citation locations atperiodic times Δt

Loc+Δα—location of said violation within +/−Δα distance, Δα preferablyranges from 100 feet to 50 miles. Loc preferably also includesdirection.

dbe(loc, nΔt+Δε)—represents a database entry which has a matchinglocation loc and issue time nΔt+Δε.

Nmin—the earliest index for calculating correlation time duration.

Nmax—the last index for calculating correlation time duration.

nt+Δε—time of said violation within +/−Δε time, Δε preferably rangesfrom 1 minute to 1 day.

Equation 5 preferably computes the total number of periodic occurrencesof citations issued at a given location loc at the periodic timesstarting at time nmin*Δt+Δε.to nmax*Δt+Δε. This could be accomplished byaccessing the database records 222 dbe and summing the number of entrieswith matching loc and time n*Δt+Δε. The occurrence of each matching dbepreferably has a weight of one. Said periodic times n*Δt+Δε could have aΔε term added which functions to allow a dbe with matching loc and inthe span of +/−ε to match. Δε preferably has a span of 1 minute to 24hours such that any dbe with a matching location loc, and issue time notwithin the said span Δε would preferably produce a positive matchresult. The patrol schedule estimator 205 preferably repeats computationof Equation 5 for each location 223 in the geographic region ofinterest. Furthermore, the patrol schedule estimator operation 205preferably repeats computation of equation 5 for different time spacingintervals Δt preferably ranging from one hour to one year in an attemptto identify periodic correlations between issued citations times andlocations. Said citation location-time correlation results 213 of thepatrol schedule estimator 205 are preferably passed to the Indicationoperation 207 and or the vehicle control operation 226.

The algorithms presented in Equations 4a, 4a1, 4b, 5, 5a and FIG. 4 areexamples of preferred methods to estimate and predict traffic lawenforcement patrol schedules from historic traffic law enforcement andcrowd sourced records; however, other algorithms to estimate traffic lawenforcement patrol schedules are possible but should be consideredwithin the scope of the present invention including using records ofpreviously determined, estimated and predicted traffic law enforcementpatrol schedules.

In yet another preferred embodiment, the patrol schedule estimator 205may utilize records similar to Equation 1a, of previously determinedpredicted locations of traffic law enforcement patrols 222, to predictpatrol schedules 215.

Patrol Enforcement Profile Estimator Operation 206

The patrol enforcement profile estimator, operation 206, preferablydetermines the enforcement profile of traffic law enforcement. Table 1provides a listing of estimated traffic law enforcement properties andpatterns which may be provided by the current invention, and theestimation of Patrol Profile properties of Table 1 may be determined bythe Patrol Enforcement Profiler operation 204. In a preferredembodiment, the patrol enforcement profile estimator 206, determines thetraffic law enforcement profile 214 at a plurality of locations withinthe geographic region and time intervals of interest 223 utilizingdatabase records derived from historic traffic law enforcement citationrecords and or crowd sourced encounters 222. In yet another preferredembodiment, the patrol profile estimator 206 utilizes records, similarto Equation 1a and Table 1, of previously predicted profiles of trafficlaw enforcement patrols 222, to predict traffic law enforcement patrolprofiles 214, at a plurality of locations within the region of interest223.

In a preferred embodiment, the traffic law enforcement profile estimator206 may estimate traffic law enforcement profile properties 214 at aplurality of locations and times within the region and time frame ofinterest 223 which are summarized in Patrol Profile of Table 1 and mayinclude any combination of: enforced speed limit, speed trap location,red light camera location, speed camera location, aircraft speed limitenforced location, histogram of issued citation types, location citationdensity, enforcement profile of traffic laws, histogram of citationspeeds for speeding citations, number of issued citations, thedistribution of enforced traffic laws, probability of receiving atraffic citation, probability of encountering traffic law enforcement,and the austerity with which traffic laws are enforced.

The austerity with which traffic laws are enforced may comprise those inTable 1 including enforcement leniency profile, speed limit enforcementausterity relative to average traffic flow velocity, locationenforcement austerity relative to average traffic volume, speed limitenforcement austerity relative to average traffic volume and otherausterity metrics and combinations thereof, and indicate said pluralityof profile properties on 214. Those skilled in the art will recognizethat additional traffic law enforcement profile characteristics can bederived from historic citation records and could be implemented with theprogrammable patrol profile estimator 206, and should be consideredwithin the scope of the present invention.

In a preferred embodiment the patrol profile estimator 206, may utilizecorrelation methods analogous to Equation 4a, 4a1 and statisticalanalysis to estimate the historical patterns and properties provided inTable 1, of traffic law enforcement from historical records of citationand encounters 222. The patrol profile estimator 206 may correlateproperties derived from citation records 222, analogous to Equation 1,which could include citation record: violation speed, time, date,location, travel direction, citation type, traffic volume, and averagetraffic velocity to estimate historical and predicted enforcementproperties of traffic law enforcement 214, as presented in Table 1, atthe current location 225 or surrounding locations within the region ofinterest 223 of apparatus 14.

In yet another preferred embodiment, the enforcement profiler 206 mayaccess previously computed predicted traffic law enforcement profiles222 analogous to Equation 1a or Table 1, at a plurality of locationswithin the region of interest 223, to provide predicted enforcementprofile properties presented in Table 1. Alternatively or additionally,the enforcement profiler 222 may access historic records derived fromissued citations and crowd sourced law enforcement encounters 222analogous to Equation 1, at a plurality of locations with the region ofinterest 223, to compute the predicted enforcement profile properties ofTable 1. The enforcement profiler 206 may provide combinations of theenforcement profile properties in Equation 1a, and Table 1 and morespecifically, the enforcement profiler 206, may provide any combinationsof the following enforcement profiles properties:

Enforcement Profile Properties:

Enforced Speed Limit

In a preferred embodiment the enforcement profile estimator 206 mayestimate the enforced speed limit at a plurality of locations and timeperiods within the region and time interval of interest 223 and providean indication of the estimated enforced speed limit on 214. The enforcedspeed limit 214 is equal to or higher than the posted speed limit and ispreferably the maximum speed at which there is not a statisticallyunacceptable chance of being cited for speeding by traffic lawenforcement. The enforced speed limit at a location may vary with thetime of day, direction of travel, traffic flow volume and time of monthor year and other events. The patrol profile estimator 206, may utilizeEquations 4a, 4a1 to correlate citation records 222 and determine thehistorical patterns and relationship of enforced speed limit tolocation, travel direction, time, date, traffic volume, and averagetraffic velocity. The patrol profile estimator 206 may also timeextrapolate from the historical patterns of enforced speed limit toinfer the enforced speed limit 214 at the location, time, date, andtravel direction 225 of apparatus 14.

In a preferred embodiment, the patrol enforcement profiler 206, cancompute a histogram of citation speeds from records 222 of issuedtraffic law enforcement citations at location 225 of apparatus 14 or aplurality of locations within the region of interest 223. Said histogramof citation speeds provides a representation of speed limit enforcementprofile for interpretation by a user and or the histogram could befurther processed by the enforcement profiler 206 to determine anestimate of the enforcement speed limit 214 possibly as the maximumpositive slope of the histogram.

In another preferred embodiment, the enforcement profiler operation 206may determine the estimated enforced speed limit 214 as the minimumspeed for which a speeding citation was issued from records 222 ofissued speeding citations at the corresponding location and preferablytime. Furthermore, the enforcement profiler operation 206 mayadditionally or alternatively estimate the enforced speed limit 214 asthe average of citation speeds of records 222 for which speedingcitations were issued at the corresponding location and preferably time.In a preferred embodiment, the enforcement profiler operation 206 mayalso calculate the variance of the violation speeds of speedingcitations records 222 at location 225 of apparatus 14 or a plurality oflocations 223 which represents the variability and uncertainty in theestimated enforced speed limit traffic law enforcement profile.

In addition to the presented methods of estimating the enforced speedlimit, the enforcement profiler 206 is preferably programmable and mayimplement additional methods of estimating said enforced speed limitfrom historical records of traffic law enforcement citations and crowdsourced encounters which should be considered within the scope of thecurrent invention. The traffic law enforcement profiles computed byoperation 206 are preferably presented on 214.

In a preferred embodiment, the enforcement profiler 206 may calculatethe mean citation speed from records of law enforcement citations andcrowd sourced data 222 is shown in equation 6. Equation 6 can be derivedfrom the generalized correlation Equations 4a, 4a1 as a constrainedcorrelation and represents an algorithm to determine the mean citationspeed for which speeding citations were issued as a function of time andlocation.

$\begin{matrix}{{{{mean\_ citation}{\_ speed}\mspace{11mu}\left( {{loc},t} \right)} = {{1/N}{\sum\limits_{matching}{{{dbe}.{speed}}\mspace{11mu}\left( {{{loc} + {\Delta\alpha}},{t + {\Delta ɛ}}} \right)}}}}{A\mspace{14mu}{preferred}\mspace{14mu}{constrained}\mspace{14mu}{correlation}\mspace{14mu}{method}\mspace{14mu}{to}\mspace{14mu}{determine}\mspace{14mu}{the}\mspace{14mu}{mean}\mspace{14mu}{citation}\mspace{14mu}{{speed}.}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

The following terms of Equation 6 are defined:

Dbe.speed—represents database entry citation speed for which thespeeding citation was issued.

Loc+Δα—location of said violation within +/−Δα distance, Δα preferablyranges from 100 feet to 10 miles.

T+Δε—time of said violation within +/−Aε time, Δε preferably ranges from1 minute to 1 day.

1/N—N is the total number of dbe entries which match at said locationand time

In a preferred embodiment, the enforcement profiler 206 may additionallyor alternatively compute the citation speed variance as a function oflocation and time from traffic law enforcement records and or crowdsourced records 222 and an example algorithm is shown in equation 7.Said variance may indicate the uncertainty in the enforced speed limitor speed limit traffic law enforcement profile. Said citation varianceresults are indicated on 214 and are preferably provided to theIndication operation 207 and or the vehicle control operation 226.citation_speed_variance(loc,t)=1/NΣ(dbe.speed(loc+Aα,t+Δε)−mean_citation_speed(loc,t))**2

Equation 7. A preferred method to determine citation speed variance as afunction of location and time.

Citation_speed_variance—variance of violation speed for which citationswere issued at the specified location and time.

dbe.speed-database entry violation speed at the specified location andtime.

Loc+Δα—location of said violation within +/−Δα distance, Δα preferablyranges from 100 feet to 10 miles.

T+Δε—time of said violation within +/−Δε time, Δε preferably ranges from1 minute to 1 day.

1/N—N is the total number of dbe entries which match at said locationand time

In yet another preferred embodiment, the enforcement profiler 206 maydetermine the enforced speed limit as minimum citation speed preferablyas a function of location and optionally time and or direction from therecords of traffic law enforcement citations and crowd sourcedencounters 222 and a preferred algorithm is shown in Equation 8. Saidminimum citation speed provides an estimation of enforced speed limit bytraffic law enforcement as a function of location and optionally timeand or direction and an estimation of the maximum speed to avoidreceiving a citation.min_citation_speed(loc,t)=floor(dbe.speed(loc+Δα, t+Δε))

Equation 8. A Preferred Method to Determine Minimum Citation Speed.

Min_citation_speed—minimum violation speed for which a citation has beenissued at the specified location and time.

dbe.speed-database entry violation speed at the specified location andtime.

Loc+Δα—location of said violation within +/−Δα distance, Δα preferablyranges from 100 feet to 10 miles.

T+Δε—time of said violation within +/−Δε time, Δε preferably ranges from1 minute to 1 day.

In equation 8, the floor function accesses dbe entries 222 at thelocation 225 of apparatus 14 or within the region of interest 223 andextracts the lowest speed field from the set of dbe records. Saidminimum citation speed results 214 of operation 206 are preferablyprovided on 214.

The algorithms presented in Equation 6, 7, and 8 are examples ofpreferred methods to estimate traffic law enforcement profile fromhistoric traffic law enforcement and crowd sourced records; however,other algorithms to estimate traffic law enforcement profile fromrecords of traffic law enforcement citations and crowd sourcedencounters 222 are possible and should be considered within the scope ofthe present invention.

Those skilled in the art will recognize that additional enforced speedlimit profiling characteristics can be derived from historic citationrecords and crowd sourced encounters of traffic law enforcement 222, anddatabases 2, 4, 5, 30, and could be implemented with the programmablepatrol profile estimator 206, and should be considered within the scopeof the present invention. Additionally, those skilled in the art willrecognize that the enforced speed limit estimate could be pre computedfor locations 225, 223 and stored in the databases 2,4,5,30 and providedas records 222 which could then be provided by the patrol profileestimator 206 as indication 214.

Histogram of Citation Speeds:

In a preferred embodiment, the enforcement profiler 206 may determine ahistogram of violation speeds for which citations 222 were issued atlocation 225 of apparatus 14 and at a plurality of locations within thetime intervals and region of interest 223 and the citation violationspeed histograms may be indicated on 214. The histogram may provide astatistical representation of the distribution of speeding citationviolation speeds. The enforcement profiler 206, preferably determinesthe histogram of speeds at a location for which speeding citations wereissued by indicating the distribution of the accumulation of eachcitation violation speed derived from records of issued speedingcitations 222 at the corresponding citation location. The histogram ofcitation violation speeds may provide an enforcement profile of theenforced speed limit at the associated location. Furthermore, theenforcement profiler 206 may determine the speed at which the histogramof violation speeds achieves maximum positive slope to estimate theenforced speed limit 214. Additionally, those skilled in the art willrecognize that histogram of citation speeds could be pre computed forlocations 225, 223 and stored in the databases 2,4,5,30 and provided asrecords 222 which could then be provided by the patrol profile estimator206 as indication 214.

Histogram of Enforced Traffic Laws:

In a preferred embodiment, the enforcement profiler 206 may determine ahistogram of enforced traffic laws for which citations were issued atthe location of apparatus 225 and a plurality of locations within thetime intervals and region of interest 223 and the histogram of enforcedtraffic laws may be indicated on 214. The histogram of enforced trafficlaws 214, may provide a statistical estimation of traffic laws which areenforce at a location. The enforcement profiler 206, preferablydetermines the histogram of enforced traffic laws at a location byindicating the distribution of the issued citation types from records ofissued speeding citations 222 at the corresponding citation location.Citation types could consist of both moving and non-moving citationviolation types such as speeding, expired registration, invalid displayof license plate for example. The histogram of enforced traffic laws mayprovide an enforcement profile of traffic laws. Additionally, thoseskilled in the art will recognize that the histogram of enforced trafficlaws could be pre computed for locations 225, 223 and stored in thedatabases 2,4,5,30 and provided as records 222 which could then beprovided by the patrol profile estimator 206 as indication 214.

Enforcement Profile of Traffic Laws:

In a preferred embodiment, the enforcement profiler 206 may estimate theenforcement profile of traffic laws which preferably consists ofestimating combinations of the enforcement schedule and enforcementausterity for different types of traffic citations and provide anindication on 214. The enforcement profiler 206 may determine theenforcement profile of traffic laws at the location 225 of apparatus 14or at a plurality of locations within the region and timeframe ofinterest 223 by computing the correlation between properties ofhistorically issued citations 222 which may include combinations ofissue time, date, location, citation warning, and citation type. In apreferred embodiment, the enforcement profiler 206 may utilize themethod of Equation 4a, 4a1 to compute the correlation between citationproperties to determine the enforcement profile of traffic laws.Additionally, those skilled in the art will recognize that theenforcement profile of traffic laws could be pre computed for locations225, 223 and stored in the databases 2,4,5,30 and provided as records222 which could then be provided by the patrol profile estimator 206 asindication 214.

Location Citation Density

The location citation density may be determined as the frequency ofissued citations which are correlated to a geographic location and mayprovide a statistical indication of the extent to which a location ispatrolled. In a preferred embodiment, the enforcement profiler 206 maydetermine the density of issued citations 222 at locations within thetime intervals and region of interest 223 and provide an indication ofthe location citation density on 214. The enforcement profiler 206,preferably determines the citation density by accumulating the citationsissued at each location at the current location 225 or at locationswithin the region of interest 223 which are derived from records ofissued citations 222 and may be normalized by traffic volume and mayprovide an indication of location patrol intensity. The locationcitation density could be determined as a function of citation type andtime may provide a geographical enforcement profile. Additionally, thoseskilled in the art will recognize that location citation density couldbe pre computed for locations 225, 223 and stored in the databases2,4,5,30 and provided as records 222 which could then be provided by thepatrol profile estimator 206 as indication 214.

Speed Trap Enforcement Location:

In a preferred embodiment, the enforcement profiler 206 may utilizecombinations of records of historic issued citations and crowd sourcedencounters 222 to predict the locations of speed traps within the regionand time intervals of interest 223 and provide an indication on 214.

In a preferred embodiment, the enforcement profiler 206 may determinethe location of estimated speed traps as locations with any statisticalcombination of: a region with a statistical increase in locationcorrelation of issued citations relative to surrounding regions, anincrease in location citation density relative to surrounding areas,drop in estimated enforced speed limit, an increase in probability of avehicle having an encounter with traffic law enforcement, or an increasein relative ratios of issued traffic citations to traffic volume thatexceed a programmable threshold relative to the ratios of issued trafficcitations to traffic volume in surrounding locations. Furthermore, in apreferred embodiment, the enforcement profiler 206, in addition topredicting the location of speed traps, may also predict the schedulesof speed trap enforcement by utilizing the patrol schedule estimates 213from the patrol schedule estimation 205. Additionally, or alternativelythe schedule of speed trap enforcement at speed trap locations can bepredicted by determining said ratios of issued traffic citations 222 totraffic volume at historic times and locations and then correlating saidratios as a function of time to identify patrol schedules which are timecorrelated, and then time extrapolating said correlation to predict saidspeed trap enforcement schedules.

Those skilled in the art will recognize that additional speed trapdetection profiling characteristics can be derived from historiccitation records and could be implemented with the programmable patrolprofile estimator 206, and should be considered within the scope of thepresent invention. Additionally, those skilled in the art will recognizethat locations of speed traps could be pre computed for locations 225,223 and stored in the databases 2,4,5,30 and provided as records 222which could then be provided by the patrol profile estimator 206 asindication 214.

Aircraft Speed Enforcement Location:

In a preferred embodiment, the enforcement profiler 206 may utilizecombinations of records of historic issued citations and crowd sourcedencounters 222 to predict the locations of aircraft speed limitenforcement within the region and time intervals of interest 223 andprovide an indication on 214.

In a preferred embodiment, the enforcement profiler 206 may determinethe location of estimated aircraft speed limit enforcement locationswith any combination of: a region with a statistical increase inlocation correlation of issued citations relative to surroundingregions, relative to surrounding areas an increase in location citationdensity, drop in estimated enforced speed limit, an increase inprobability of a vehicle having an encounter with traffic lawenforcement, or an increase in relative ratios of issued trafficcitations to traffic volume that exceed a programmable thresholdrelative to the ratios of issued traffic citations to traffic volume insurrounding locations. Additionally, or alternatively, the enforcementprofiler 206 may determine locations where the speed limit is enforcedby aircraft by examining the citation type of citation records presentedin Equation 1 as being issued by aircraft enforcement and utilizing thecitation location correlation as the location of the aircraft speedlimit enforced location.

Furthermore, in a preferred embodiment, the enforcement profiler 206, inaddition to predicting the location aircraft speed limit enforcement,may also predict the schedules of aircraft speed limit enforcement byutilizing the patrol schedule estimates 213 from the patrol scheduleestimation 205. Additionally, or alternatively the schedule of aircraftspeed limit enforcement at patrolled locations can be predicted bydetermining said ratios of issued traffic citations 222 to trafficvolume at historic times and locations and then correlating, analogousto Equation 4a, 4a1, said ratios as a function of time to identifypatrol schedules which are time correlated, and then time extrapolatingsaid correlation to predict said aircraft speed limit enforcementschedules.

Those skilled in the art will recognize that additional aircraft speedlimit enforcement detection profiling characteristics can be derivedfrom historic citation records and could be implemented with theprogrammable patrol profile estimator 206, and should be consideredwithin the scope of the present invention. Additionally, those skilledin the art will recognize that locations of aircraft speed limitenforcement could be pre computed for locations 225, 223 and stored inthe databases 2,4,5,30 and provided as records 222 which could then beprovided by the patrol profile estimator 206 as indication 214.

Red Light Camera Enforced Intersection:

In a preferred embodiment, the enforcement profiler 206 may utilizecombinations of records of historic issued citations and crowd sourcedencounters 222 to predict the locations of intersections with red lightcamera enforcement within the region and timeframe of interest 223 andprovide an indication on 214. The enforcement profiler 206 may determinelocations of intersections equipped with red light camera enforcement byexamining the citation type of citation records presented in Equation 1as being issued by automated red light camera enforcement and utilizingthe citation location correlation as the location of the red lightenforced intersection. Alternatively or additionally, the enforcementprofiler 206 may determine locations of intersections equipped with redlight camera enforcement by observing the relative density of citationsissued 222 at intersections located within the region and time frame ofinterest 223, and infer the locations of intersections equipped with redlight camera enforcement as those with a number of citations issued oftype similar to red light violation exceeding a programmable thresholdgreater than the number of issued citations of similar type atsurrounding intersections. Additionally, red light camera enforced areasmay be indicated by a region with a statistical increase in locationcorrelation of issued red light violation citations relative tosurrounding regions.

Those skilled in the art will recognize that additional red light cameraenforced detection profiling characteristics can be derived fromhistoric citation records and could be implemented with the programmablepatrol profile estimator 206, and should be considered within the scopeof the present invention. Additionally, those skilled in the art willrecognize that red light camera locations could be pre computed forlocations 225, 223 and stored in the databases 2,4,5,30 and provided asrecords 222 which could then be provided by the patrol profile estimator206 as indication 214.

Speed Camera Enforced Location:

In a preferred embodiment, the enforcement profiler 206 may utilizecombinations of records of historic issued citations and crowd sourcedencounters 222 to predict the locations with speed camera enforcementwithin the region and time period of interest 223 and provide anindication on 214. The enforcement profiler 206 may determine locationsof roadway equipped with speed camera enforcement by examining citationtype of citation records presented in Equation 1 as being issued byautomated speed camera enforcement and utilizing the citation locationcorrelation as the location of the speed camera enforcement.Alternatively or additionally, the enforcement profiler 206 maydetermine locations of roadway equipped with speed camera enforcement byobserving the relative location citation density of issued 222 onroadway located within the region and time frame of interest 223, andinfer the locations of roadway equipped with speed camera enforcement asthose with a number of citations issued of type similar to speedingviolation exceeding a programmable threshold greater than the number ofissued citations of similar type at surrounding roadway.

Those skilled in the art will recognize that additional speed cameraenforced detection profiling characteristics can be derived fromhistoric citation records and could be implemented with the programmablepatrol profile estimator 206, and should be considered within the scopeof the present invention. Additionally, those skilled in the art willrecognize that speed camera locations could be pre computed forlocations 225, 223 and stored in the databases 2,4,5,30 and provided asrecords 222 which could then be provided by the patrol profile estimator206 as indication 214.

Enforcement Austerity

The enforcement austerity estimate provides a means to predict theamount of flexibility, leniency or austereness of traffic lawenforcement in enforcing traffic laws and may include the EnforcementLeniency Profile, Speed Limit Enforcement Austerity and LocationEnforcement Austerity.

Enforcement Leniency Profile

In a preferred embodiment, the enforcement profiler 206, may determinethe enforcement leniency profile which is a statistical measure of thewillingness of traffic law enforcement to issue warnings rather thancitations, at the location of apparatus 225 or locations within theregion and time intervals of interest 223 and provide an indication on214. The enforcement profiler 206, preferably determines the enforcementleniency profile for citation types and is derived from the ratio of thenumber of citations issued 222 of a given type to the number of warningsissued 222 of said given type at each location and time interval. Theenforcement leniency profile of issued citations may provide ageographical representation of enforcement tolerance for each type ofcitation.

Those skilled in the art will recognize that additional enforcementleniency profiling characteristics can be derived from historic citationrecords and could be implemented with the programmable patrol profileestimator 206, and should be considered within the scope of the presentinvention. Additionally, those skilled in the art will recognize theenforcement leniency profile could be pre computed for locations 225,223 and stored in the databases 2,4,5,30 and provided as records 222which could then be provided by the patrol profile estimator 206 asindication 214.

Speed Limit Enforcement Austerity Relative to Average Traffic FlowVelocity

In a preferred embodiment, the enforcement profiler 206, may estimatethe enforced speed limit austerity relative to the average flow velocityof traffic at the location of apparatus 225 or at a plurality oflocations within the region of interest 223 and provide an indication on214. The enforcement profiler may determine the speed limit enforcementausterity by correlating or comparing the average traffic flow velocityfrom historical records of traffic flow 222, contained in databases 16,23 to traffic law enforcement issued citation speeds 222 at associatedlocations and times providing the enforced speed limit relative to theflow of traffic. Preferably the enforcement profiler 206 can compare thetraffic flow rate with records of issued speeding citation velocities222 and determine the estimated velocity at which traffic lawenforcement issues citations at given times and locations relative tothe average traffic flow velocity and may determine the austerity withwhich traffic law enforcement enforces the speed limit relative toaverage traffic flow velocities and volumes at given locations andtimes.

In another preferred embodiment, the enforcement profiler 206, mayestimate the schedule of speed limit austerity relative to traffic flowvelocities at the location 225 of apparatus 14 or at a plurality oflocations within the region of interest 223 and provide an indication on214. The enforcement profiler 206, preferably determines the differencebetween the historic traffic flow average velocity from databases 16, 23at location 225 or plurality of said locations within 223 and thevelocities at which historic citations have been issued at correspondingplurality of locations and times 222. At each location, the enforcementprofiler may then cross correlate said differences as a function of timeas in Equation 4a, 4a1 to estimate the enforced speed limit austerityschedule, and time extrapolate to predict current or future enforcedspeed limit austerity schedules at corresponding locations.

Those skilled in the art will recognize that additional speed limitenforcement austerity characteristics can be derived from historiccitation records and could be implemented with the programmable patrolprofile estimator 206, and should be considered within the scope of thepresent invention. Additionally, those skilled in the art will recognizethe speed limit enforcement austerity could be pre computed forlocations 225, 223 and stored in the databases 2,4,5,30 and provided asrecords 222 which could then be provided by the patrol profile estimator206 as indication 214.

Location Enforcement Austerity Relative to Average Traffic Volume

In a preferred embodiment, the enforcement profiler 206, may estimatethe probability of receiving a citation relative to the average flowvolume of traffic at location 225 or a plurality of locations within thetimeframe and region of interest 223, which provides a measure of howmuch a location is patrolled and may be indicated on 214. Theenforcement profiler may determine the location enforcement austerityrelative to the average traffic volume by computing the probability of avehicle receiving a citation at a location derived from the ratio of thenumber of citations issued at a location and time interval to theaverage flow volume of traffic at the corresponding location and timeinterval.

Those skilled in the art will recognize that additional locationenforcement austerity profiling characteristics can be derived fromhistoric citation records and could be implemented with the programmablepatrol profile estimator 206, and should be considered within the scopeof the present invention. Additionally, those skilled in the art willrecognize the location enforcement austerity profile could be precomputed for locations 225, 223 and stored in the databases 2,4,5,30 andprovided as records 222 which could then be provided by the patrolprofile estimator 206 as indication 214.

Speed Limit Enforcement Austerity Relative to Average Traffic Volume

In a preferred embodiment, the enforcement profiler 206, may estimatethe enforced speed limit austerity relative to the average flow volumeof traffic at the location of apparatus 225 or at a plurality oflocations within the region of interest 223 and provide an indication on214. The enforcement profiler may determine the speed limit enforcementausterity by comparing the average traffic flow volume from historicalrecords of traffic flow 222, contained in databases 16, 23 to trafficlaw enforcement issued citation speeds 222 at associated locations andtimes providing the enforced speed limit relative to the flow oftraffic. Preferably the enforcement profiler 206 can compare the trafficflow rate with records of issued speeding citation velocities 222 anddetermine the estimated velocity at which traffic law enforcement issuescitations at given times and locations relative to the average trafficflow velocity and may determine the austerity with which traffic lawenforcement enforces the speed limit correlated to the average trafficflow velocities and volumes at given locations and times.

Those skilled in the art will recognize that additional speed limitenforcement austerity profiling characteristics can be derived fromhistoric citation records and could be implemented with the programmablepatrol profile estimator 206, and should be considered within the scopeof the present invention. Additionally, those skilled in the art willrecognize the speed limit enforcement austerity profile could be precomputed for locations 225, 223 and stored in the databases 2,4,5,30 andprovided as records 222 which could then be provided by the patrolprofile estimator 206 as indication 214.

Those skilled in the art will recognize that in addition to theenforcement profiles outlined in the present invention, additionalenforced profiles could be derived from historic records of citationsand crowd sourced encounters and could be implemented with theprogrammable patrol profile estimator 206, and should be consideredwithin the scope of the present invention.

Presentation of Enforcement Schedules, Locations, Profiles

Indication operation 207 provides a means to present the records ofhistorical traffic law enforcement citations and crowd sourced events222 and or estimated traffic law enforcement locations 215, schedules213 and profile 214 to a user. In a preferred embodiment, indicationoperation 207 preferably accepts estimated patrol locations 215,estimated patrol schedules 213, the estimated traffic law enforcementprofile 214, historic records of traffic law enforcement citations 222,and roadway map database entries 222 and apparatus speed, time, locationand direction data 225 to present a representation which may includecombinations of estimated traffic law enforcement locations 215,schedules 213, enforcement profile 214, historic traffic law enforcementrecords 222 and the traffic law enforcement properties presented inTable 1. Said presentation of estimated traffic law enforcementlocations, schedules, enforcement profile and historic data may includeaudio 8 and or visual means 9.

Preferred Presentations

In a preferred embodiment, FIG. 4 presents a view 400 of display 9preferably produced by the indication operation 207. Many variations ofview 400 are possible, yet still provide combinations of historic andestimated traffic law enforcement characteristics which may includehistoric traffic law enforcement records 222, estimated traffic lawenforcement locations 215, estimated schedules 213, and enforcementprofile 214 and combinations thereof, and should be considered withinthe scope of the present invention. View 400 preferably presents anumerical representation of the estimated enforced speed limit 401 atthe current location 225 of apparatus 14 and could optionally alsopresent the estimated enforced speed limit at a plurality of points,preferably limited to less than 1000, further ahead or behind thedirection of travel or in the vicinity of the apparatus 225. Items 403and 404 indicate representative examples of the estimated enforced speedlimit at a point 1 mile ahead and at a point 2 miles ahead respectively.In other preferred embodiments, fewer or additional estimated speedlimits at other locations could be displayed over what is presented inthe preferred view 400. Additionally or alternatively, a histogram or adistribution 402 could be presented at a plurality of locations,preferably less than 1000, which communicates the distribution of speedsat which citations 222 have been issued at said plurality of locationsfor the time period of interest 223. A histogram 402 provides aconvenient means for a user to rapidly determine the enforcement profileand infer the enforced speed limit at locations of interest 223.Additionally or alternatively, view 400 may have indications for thelocations of a plurality, preferably less than 1000, of estimated speedtraps 405, or speed cameras 406 which were indicted on 215, 213 and 214.In a preferred embodiment, audio output 8 may enable audio indicationsof a plurality of estimated speed limits 401, 403, 404, and speed traps405 in addition to or in place of displayed indications 401, 403, 404,405 in which case display 9 may not be present.

In yet another preferred embodiment, FIG. 5 presents another exampleview 64 of display 9. In view 64 of display 9, the indication operation207 may combine a roadway map, preferably from databases 15 and or 17,with any combination of said predicted traffic law enforcement locations215, schedules 213, enforcement profile 214 and historic traffic lawenforcement records 222. Many variations of view 64 are possible, yetstill provide combinations of estimated traffic law enforcementlocations, schedules, and enforcement profile and historical traffic lawenforcement data combined with a map of the roadway and should beconsidered within the scope of the present invention. View 64demonstrates a preferred embodiment of many different display optionsfor proving estimated and historic traffic law enforcement data to thedriver, and preferably view 64 could be customizable to provide a subsetor alterations of the data which the operator may select.

View 64 presents an example map of the roadway 50, 59, and 58. Overlaidon the roadway 50, is preferably the current location of the user 62,and optionally any of the current velocity, enforced speed limit, postedspeed limit, the time 63 of the user 62. Preferably overlaid on theroadway 50 could be a plurality of indications 51, 54, and 57 showingestimated enforcement speed limit 214 derived from historical records ofcitations issued by traffic law enforcement or crowd sourced encountersat corresponding locations which were preferably calculated by the speedlimit enforcement profiler 206. Said plurality of estimated enforcementspeed limits 51, 54, 57 can vary and be correlated by time of day, date,direction, and location, traffic conditions and traffic flow rates andthe patrol schedule can optionally be presented for correspondinglocations 215. Furthermore, in a preferred embodiment, various methodscould be utilized for conveying said plurality of enforcement speeds 51,54, 57 including color or gradient coding sections of roadway tocorrespond to enforced speed limit ranges as indicated by 58, 83, 84, 85or numerical representations 51, 54, 57. Additionally, various methodscould be utilized for conveying location citation density whichindicates the intensity of patrols at a location as could be indicatedby 58, 83, 84, 85 wherein the shade or color of sections of roadwaycould encode the level of patrol intensity. Preferably overlaid on theroadway 50 could be a plurality of indications 72 showing estimatedenforcement austerity 214 at corresponding plurality of locations. Saidausterity 72 could preferably include providing combinations of theenforcement leniency profile, speed limit enforcement austerity relativeto average traffic flow velocity, location enforcement austerityrelative to average traffic volume, speed limit enforcement austerityrelative to average traffic velocity and other austerity metrics of thepresent invention and those presented in Table 1.

Additionally, example symbols 52, 53, 55 and 56 may represent aplurality, of predicted patrol locations 215 over a time period andlocations of interest 223, preferably determined by the Patrol LocationEstimator 204, which identifies the locations where traffic lawenforcement has historically issued citations. In a preferred embodimentof view 64, the locations of a plurality of historically issuedcitations or crowd sourced encounters 222, limited to the region ofinterest 223, may alternatively or additionally be displayed as shown byexample symbols 73 and 74. Preferably the presentation of said pluralityof historically issued citations 222 may be filtered by combinations ofcitation type, time, location and direction. In view 64, historicallyissued speeding citations 222 over a time period and region of interest223 could be displayed as a histogram at a plurality of locationslimited to the region of interest 223, examples of which include 70 and71 which present the citation speed verses the number of speedingcitations issued at said citation speed at said plurality of locations.Further, view 64 may identify a plurality of locations, within theregion of interest 223, as speed traps, red light cameras, speedcameras, and aircraft enforced locations as indicated on 214, whichpreferably may be presented overlaid on view 64 as 55, 80, 81, 82respectively. Other indications could be employed by the presentoperation 207 to identify speed traps, red light camera locations andspeed camera locations and should be considered within the scope of thecurrent invention.

View 64 can provide an indication of estimated traffic law enforcementpatrol schedules 213 at a plurality of locations within the region ofinterest 223 as example indications 60, 65. Estimated patrol scheduleindications 60, 65 preferably provide estimated patrol times, locations,and may preferably but not necessarily also provide any combinations ofestimated enforced speed limit, the average speed at which citationswere issued, citation variance from the mean, the minimum speed forwhich a speeding violation was issued, and a histogram of speeds forissued speeding citations. Alternative representations of 60,65estimated traffic law enforcement patrol schedules 213 are possible andshould be considered within the scope of the present invention.

The speed limit enforcement profile 214, patrol schedules 213, andpatrol locations 215 can preferably be used in conjunction with roadnavigation planning to select a route between destinations with thefastest estimated enforcement speed limit to minimize driving time.

Additionally, the apparatus 14 and view 64 may preferably provide a setof historical citation and crowd sourced record database 2, 4, 5, 30processing methods to enable searching, filtering, extracting,statistical processing, and viewing of historical traffic lawenforcement citation and crowd sourced records preferably includinghistogram creation, distributions, scatter plots, tables and lists.

View 64 is an example of one realization to present the traffic lawenforcement profiled information and historical records of citations andcrowd source encounters, and many alterations of the above descriptionare possible but still within the scope of the current invention.

While the above description contains many specifics, these should not beconstrued as limitations on the scope of the invention, but rather as anexemplification of one preferred embodiment thereof. It will be obviousto those skilled in the art that many modifications and alterations maybe made without departing from the spirit and scope of the inventionwhich should be determined not by the embodiments illustrated, but bythe appended claims and their legal equivalents.

What is claimed is:
 1. An apparatus for utilizing real time and/orestimated properties of traffic law enforcement within a regioncomprising: a location determining system; a database storing at leastrecords comprising properties of traffic law enforcement and one or bothof records of issued citations of traffic law enforcement and/or recordsof crowd sourced traffic law enforcement encounters, for at least saidregion; wherein said records of properties of traffic law enforcementcomprise one or more of: an estimated patrol location, an estimatedpatrol schedule, and real time patrol locations, and wherein saidrecords are derived by analysis of properties from either one or both of(a) records of issued citations of traffic law enforcement, and (b)records of crowd sourced traffic law enforcement encounters; apredictive processor configured to: retrieve all or a portion of anysaid records within said region from said database, determine saidproperties of traffic law enforcement by filtering and/or analyzing allor a portion of said retrieved records, and based on said determinedproperties of traffic law enforcement, provide one or more vehiclecontrol outputs to at least control a speed and/or a direction ofmovement for a vehicle within said region; a vehicle controller for saidvehicle configured to receive said one or more vehicle control outputsfrom said predictive processor and to control said vehicle accordingly;and a user control, operated by either a machine user or a human user inconjunction with said machine user, and at least utilizing both alocation measurement taken by said location determining system and saidprovided one or more vehicle control outputs from said predictiveprocessor, configured to control operation of said vehicle controller aswell as one or both of (a) what of any said records are gathered,stored, analyzed, and/or filtered by said database and/or by saidpredictive processor, and (b) said one or more vehicle control outputsprovided by said predictive processor.
 2. Said apparatus of claim 1,said apparatus further comprising: a speed determining system todetermine a speed of said vehicle; wherein said records further compriseone or more of the following: an estimated enforced speed limit, aposted speed limit, real time traffic flow rates, and historical trafficflow rates; and wherein said vehicle controller controls said speed ofsaid vehicle relative to one or more of said estimated enforced speedlimit, said posted speed limit, said real time traffic flow rates, andsaid historical traffic flow rates.
 3. Said Apparatus of claim 1, saidapparatus further comprising: a sensor to identify encounters withtraffic law enforcement; and wherein said predictive processor utilizessaid sensor to identify and report said encounters with traffic lawenforcement to said database for storage.
 4. Said apparatus of claim 1,said apparatus further comprising: an indicator configured to presentsaid properties of traffic law enforcement; and wherein said indicatorcomprises any combination of audio, visual, computer, and networkinterfaces.
 5. Said apparatus of claim 1, said apparatus furthercomprising: a time determining system to determine a time and date; andwherein said predictive processor determines said properties of trafficlaw enforcement relative to said time and date.
 6. Said apparatus ofclaim 1, said apparatus further comprising: a direction determiningsystem to determine a direction of travel of said vehicle; and whereinsaid predictive processor determines said properties of traffic lawenforcement relative to said direction of travel of said vehicle. 7.Said apparatus of claim 1, wherein said properties from said records ofissued citations of traffic law enforcement and/or said crowd sourcedtraffic law enforcement encounters comprises one or more members of thegroup consisting of: travel direction, speed, location, encounter time,and encounter or citation type.
 8. Said apparatus of claim 1, whereinsaid records of properties of traffic law enforcement further compriseone or more members of the group consisting of: estimated enforced speedlimit, posted speed limit, real time traffic flow rates, historicaltraffic flow rates, estimated speed trap enforced location, estimatedspeed limit enforcement by aircraft location, estimated speed cameraenforced location, estimated red light camera enforced intersection,histogram of enforced traffic laws, location citation density, locationpatrol intensity, enforcement austerity, and enforcement leniency. 9.Said apparatus of claim 1, said apparatus further comprising: anindicator configured to present said properties of traffic lawenforcement; wherein said database further stores a map of a roadwaysystem; wherein said indicator further comprises a display configured topresent said map of said roadway system; wherein said presentation ofsaid map of said roadway system is in place of or in addition to any orall of said estimated properties of traffic law enforcement; and whereinsaid display indicates any or all of said estimated properties oftraffic law enforcement relative to said location measurement taken bysaid location determining system on said map of said roadway system. 10.Said apparatus of claim 1, wherein: said database further stores a mapof a roadway system; wherein said records of properties of traffic lawenforcement further comprise one or more of: an estimated enforced speedlimit, a posted speed limit, real time traffic flow rates, andhistorical traffic flow rates; wherein said user control is configuredto further accept route guidance inputs comprising at least a startinglocation, and a destination location each located within boundaries ofsaid map of said roadway system; wherein said predictive processorfurther comprises one or more routing algorithms configured to utilizesaid starting location and said destination location to determine anoptimum route from among one or more candidate routes from said startinglocation to said destination location; wherein said optimum route isdetermined by ranking said one or more candidate routes against eachother by utilizing specific criteria inputted into said user controlcomprising weighting factors associated with at least a routeprobability of citation and a route time efficiency; wherein said one ormore routing algorithms utilize any of said records of properties oftraffic law enforcement within said region along said one or morecandidate routes in order to determine said optimum route; and whereinsaid vehicle controller controls in one or more of the following ways:(a) controls said vehicle movement direction to follow and/or not followsaid optimum route and/or said one or more candidate routes, (b)modifies said optimum route and/or said one or more candidate routes,(c) executes specific vehicle control actions at specific locationsalong said optimum route and/or said one or more candidate routes, and(d) controls said vehicle speed relative to one or more of saidestimated enforced speed limit, said posted speed limit, said real timetraffic flow rates, ands said historical traffic flow rates along saidoptimum route and/or said one or more candidate routes.
 11. Saidapparatus of claim 1, said apparatus further comprising: one or morenetworks; a smart phone or tablet configured to control said vehiclecontroller through said one or more networks; wherein said databasecomprises two or more at least partly physically separate databases;wherein said properties of traffic law enforcement are retrieved by saidpredictive processor from said two or more at least partly physicallyseparate databases through said one or more networks; and wherein saiduser control comprises an application on said smart phone or tablet. 12.Said apparatus of claim 1, said apparatus further comprising: one ormore networks; wherein said database comprises two or more at leastpartly physically separate databases; wherein said predictive processorcomprises two or more at least partly physically separate predictiveprocessors; and wherein said two or more predictive processors retrievesany of said records from said two or more databases through said one ormore networks.
 13. Said apparatus of claim 1, said apparatus furthercomprising: one or more networks configured to enable communicationbetween said user control and said database; wherein said databasecomprises two or more at least partly physically separate databases; andwherein said user control is configured to enable said human user orsaid machine user to add a record of crowd sourced traffic lawenforcement encounter to said records of said database for storage. 14.Said apparatus of claim 1, wherein: said database further stores one ormore historical encounter records derived from said issued citationrecords of traffic law enforcement and/or crowd sourced traffic lawenforcement encounters; wherein each said historical encounter recordcomprises one or more members of the group consisting of: traveldirection, location, time, speed, and encounter or citation type;wherein said predictive processor is further configured to utilize afiltered record of historical traffic law enforcement by retrieving anysaid historical encounter records from said database matching anycombination of: location, time, date, travel direction, encounter orcitation type, and violation speed; and wherein said apparatus furthercomprises an indicator configured to present said filtered record ofhistorical traffic law enforcement.
 15. Said apparatus of claim 1, saidapparatus further comprising: a time determining system; a directiondetermining system; and a speed determining system; wherein said recordsof properties of traffic law enforcement further comprise one or moreof: estimated enforced speed limit, posted speed limit, real timetraffic flow rates, historical traffic flow rates, estimated speed trapenforced location, estimated speed limit enforcement by aircraftlocation, estimated speed camera enforced location, estimated red lightcamera enforced intersection, histogram of violation speed, histogram ofenforced traffic laws, location citation density, location patrolintensity, enforcement austerity, and enforcement leniency; and whereinsaid vehicle controller controls said vehicle speed at least during saidestimated patrol schedules, and/or at one or more of: said estimatedpatrol locations and said real time patrol locations, relative to one ormore of: said estimated enforced speed limit, said posted speed limit,said real time traffic flow rates, and said historical traffic flowrates.
 16. Said apparatus of claim 1, wherein said vehicle controllerfurther comprises one or more of the following: an adaptive cruisecontrol, a lane assist control, a semi-autonomous control, a fullyautonomous control, or any combination thereof.
 17. A method forutilizing estimated properties of traffic law enforcement at one or morelocations utilizing a database, a predictive processor, a vehiclecontroller, a location determining system, and a user control, saidmethod comprising the steps of: determining one or more locations atleast partially based on measurements taken by said location determiningsystem; retrieving from said database, by said predictive processor, allor a portion of stored records of estimated properties of traffic lawenforcement at said one or more locations, wherein said estimatedproperties of traffic law enforcement are derived by said predictiveprocessor utilizing either one or both of (a) records of issued trafficlaw enforcement citations, (b) records of crowd sourced traffic lawenforcement encounters; and wherein said estimated properties of trafficlaw enforcement comprise either one or more of: estimated patrollocations, estimated patrol schedules, and real time patrol locations;controlling one or more of a speed, and a direction of movement, of avehicle within said one or more locations, using said vehiclecontroller, based at least on said estimated properties of traffic lawenforcement; controlling operation of said vehicle controller by eithera machine user or a human user in conjunction with said machine user ofsaid user control, based on at least utilizing both said measurementstaken by said location determining system and said estimated propertiesof traffic law enforcement, and also using said user control to controlone or both of (a) what of any stored records are retrieved and/oranalyzed by said predictive processor, and (b) which estimatedproperties of traffic law enforcement are determined by said predictiveprocessor.
 18. Said method of claim 17, wherein: said estimatedproperties of traffic law enforcement further comprise one or moremembers of the group consisting of: estimated enforced speed limit,posted speed limit, real time traffic flow rates, historical trafficflow rates, estimated speed trap enforced location, estimated speedlimit enforcement by aircraft location, estimated speed camera enforcedlocation, estimated red light camera enforced intersection, histogram ofviolation speed, histogram of enforced traffic laws, location citationdensity, location patrol intensity, enforcement austerity, andenforcement leniency; and wherein the method further comprises theadditional step of: utilizing said vehicle controller to control saidvehicle speed at least during said estimated patrol schedules, and/or atone or more of: said estimated patrol locations and said real timepatrol locations, relative to one or more of: said enforced speed limit,said posted speed limit, said real time traffic flow rates, and saidhistorical traffic flow rates.
 19. Said method of claim 17, wherein saidmethod further comprises the additional steps of: utilizing a sensor anda network to autonomously detect locations of traffic law enforcement;and storing said locations of traffic law enforcement within saiddatabase.
 20. Said method of claim 17, wherein said method furthercomprises the additional steps of: utilizing a network; wherein saiddatabase comprises two or more at least partly physically separatedatabases; and wherein said predictive processor retrieves any of saidrecords of properties of traffic law enforcement at said one or morelocations through said network.
 21. Said method of claim 17, whereinsaid vehicle controller further comprises one or more of the following:an adaptive cruise control, a lane assist control, a semi-autonomouscontrol, a fully autonomous control, or any combination thereof.
 22. Anapparatus for utilizing records of real time and/or historical trafficlaw enforcement comprising: a location determining system, a databasestoring historical records derived from issued traffic law enforcementcitations and/or crowd sourced traffic law enforcement encounters;wherein each said historical record comprises one or more members of thegroup consisting of: travel direction, location, encounter time, andencounter or citation type; wherein said database further stores recordsof properties of traffic law enforcement consisting of at least one of:estimated patrol locations, estimated patrol schedules, and/or real timepatrol locations; wherein said database further stores one or more of:estimated enforced speed limit, posted speed limit, real time trafficflow rates, historical traffic flow rates, estimated speed trap enforcedlocation, estimated speed limit enforcement by aircraft location,estimated speed camera enforced location, estimated red light cameraenforced intersection, histogram of violation speed, histogram ofenforced traffic laws, location citation density, location patrolintensity, enforcement austerity, and enforcement leniency; and whereinsaid database further stores a map of a roadway system; a predictiveprocessor to retrieve and/or estimate one or more of (a) a filteredselection of said historical records conforming to a specific encounteror citation type, encounter time, location, and/or encounter or citationtravel direction, and (b) records of properties of traffic lawenforcement; a vehicle controller for a vehicle within said roadwaysystem configured to controls one or more of a speed of said vehicle anda direction of movement of said vehicle, based at least on any of saidretrieved and/or estimated historical records and/or properties oftraffic law enforcement; and a user control, operated by either amachine user or a human user in conjunction with said machine user, andat least utilizing both measurements taken by said location determiningsystem and any of said retrieved and/or estimated historical recordsand/or properties of traffic law enforcement by said predictiveprocessor, configured to control operation of said vehicle controllerand one or more of: (a) what of any said stored records are retrievedand/or analyzed by said predictive processor, and (b) which estimatedproperties of traffic law enforcement are determined by said predictiveprocessor.
 23. Said apparatus of claim 22, wherein said vehiclecontroller further comprises one or more of the following: an adaptivecruise control, a lane assist control, a semi-autonomous control, afully autonomous control, or any combination thereof.